Parameter archival electronic storage system for image processing models

ABSTRACT

Embodiments of the invention are directed to systems, methods, and computer program products for a parameter archival storage system for image processing models. The system is configured for read-optimized compression storage of machine-learning neural-network based image processing models with reduced storage by separately storing weight filter bits. The system is configured to construct weigh parameter objects associated with the plurality of neural network layers of an image processing model, such that the image processing model can be reconstructed from the weigh parameter objects. The system may discard the hierarchical linked architecture of the second image processing model and store the second image processing model at the at least one hosted model versioning system repository by storing only the weigh parameter objects.

FIELD OF THE INVENTION

The present invention generally relates to the field of machine-learningneural-network based image processing models. In particular, embodimentsof the novel present invention provides a unique, electronic queryengine for constructing a model abstraction layer configured forselection, mutation and construction of the image processing models.Moreover, embodiments of the invention also provide an electronic systemfor versioning machine-learning neural-network based image processingmodels and identifying and tracking mutations in hyper parametersamongst versions of image processing models. Additionally, embodimentsof the invention provide a parameter archival storage system configuredfor read-optimized compression storage of machine-learningneural-network based image processing models with reduced storage, byseparately storing weight filter bits.

BACKGROUND

Increasingly prevalent computers, mobile phones, smart devices,appliances, and other devices, require a variety of complex functionsinvolving image processing, identification, pattern recognition, 3Dimage reconstruction, visualization, modelling, etc. However, thesecomplex functions require complex processing capabilities that are notmet by conventional systems. Machine learning or deep learning modelsare required for these tasks. There is a need for improvedmachine-learning neural-network based models that have the ability tolearn and model non-linear and complex relationships, generalize andinfer unseen relationships, and have the ability to handle a variety ofinputs, thereby overcoming the deficiencies of conventional systems. Thepresent invention provides solutions to various problems prevalent inneural-network based machine-learning technology.

The previous discussion of the background to the invention is providedfor illustrative purposes only and is not an acknowledgement oradmission that any of the material referred to is or was part of thecommon general knowledge as at the priority date of the application.

SUMMARY OF THE INVENTION

In one aspect, the present invention is directed to in general a system,method and computer program product for a parameter archival storagesystem for image processing models, a corresponding method, and computerprogram product. The system is configured for read-optimized compressionstorage of machine-learning neural-network based image processing modelswith reduced storage by separately storing weight filter bits. Thesystem typically includes at least one hosted model versioning systemrepository comprising one or more image processing models storedthereon, wherein each of the one or more image processing models areconfigured for hierarchical processing of temporal image data via atleast one convolutional neural network. The system typically includes atleast one processing device operatively coupled to at least one memorydevice and at least one communication device connected to a distributednetwork. The system also typically includes a module stored in the atleast one memory device comprising executable instructions that whenexecuted cause the processing device and hence the system to perform oneor more functions described below.

In one embodiment, the system is configured to: receive, from a userdevice, a first user input to check-out a first image processing modelfrom the at least one hosted model versioning system repository, whereinthe first image processing model comprises a plurality of firstconvolutional neural network layers; extract the first image processingmodel of the one or more image processing models; receive, from the userdevice, a second user input associated with a request to store a secondimage processing model at least one hosted model versioning systemrepository; determine a hierarchical linked architecture associated withthe second image processing model, wherein the hierarchical linkedarchitecture comprises a sequential linked arrangement of a plurality ofsecond convolution neural network layers associated with the secondimage processing model; construct weigh parameter objects associatedwith the plurality of second convolution neural network layers of thesecond image processing model, wherein the weigh parameter objects areconstructed such that the second image processing model can bereconstructed from the weigh parameter objects; discard the hierarchicallinked architecture of the second image processing model; and store thesecond image processing model at the at least one hosted modelversioning system repository by storing only the weigh parameterobjects; and present, on a display device of the user device, anindication that the second image processing model has been stored.

In another embodiment, and in combination with any of the previousembodiments, constructing weigh parameter objects associated with thesecond image processing model further comprises: extracting a firstplurality of weights associated with the plurality of firstconvolutional neural network layers of the first image processing model;extracting a second plurality of weights associated with the pluralityof second convolution neural network layers associated with the secondimage processing model; determining altered weights in the secondplurality of weights that deviate from the corresponding first pluralityof weights; mapping the altered weights in the second plurality ofweights with the corresponding first plurality of weights and thecorresponding plurality of first convolutional neural network layers;and constructing the weigh parameter objects for the second imageprocessing model comprising the altered weights.

In another embodiment, and in combination with any of the previousembodiments, the system is configured to receive, from the user device,a third user input associated with selection of the constructed secondimage processing model for analysis. The system is further configuredto: dynamically reconstruct the second image processing model by:extracting the weigh parameter objects, and mapping the weigh parameterobjects with a stored hierarchical linked architecture of the firstimage processing model to construct the second image processing model;and present the dynamically reconstructed second image processing modelon the display device of the user device.

In another embodiment, and in combination with any of the previousembodiments, storing the second image processing model at the at leastone hosted model versioning system repository further comprises: mappinga first hyper parameter of the plurality of first convolutional neuralnetwork layers of the first image processing model with a second hyperparameter of the plurality of second convolution neural network layersassociated with the second image processing model, based on determiningthat the second hyper parameter is a mutation of the original firsthyper parameter; storing the second image processing model at the atleast one hosted model versioning system repository by storing only (i)the weigh parameter objects, and (ii) the second hyper parameter.

In another embodiment, and in combination with any of the previousembodiments, the system is configured to receive, from the user device,a third user input associated with selection of the constructed secondimage processing model for analysis. The system is further configuredto: dynamically reconstruct the second image processing model by:extracting the (i) the weigh parameter objects, and (ii) the mappedmutation in hyper parameters; and mapping the (i) the weigh parameterobjects, and (ii) the mapped mutation in hyper parameters with a storedhierarchical linked architecture of the first image processing model toconstruct the second image processing model; and present the dynamicallyreconstructed second image processing model on the display device of theuser device.

In another embodiment, and in combination with any of the previousembodiments, storing the second image processing model at the at leastone hosted model versioning system repository further comprises:constructing a pointer link between the weigh parameter objects and astored hierarchical linked architecture associated with the first imageprocessing model.

In another embodiment, and in combination with any of the previousembodiments, the system is configured to perform training of the secondimage processing model.

The features, functions, and advantages that have been discussed may beachieved independently in various embodiments of the present inventionor may be combined with yet other embodiments, further details of whichcan be seen with reference to the following description and drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

Having thus described embodiments of the invention in general terms,reference will now be made to the accompanying drawings, wherein:

FIG. 1 depicts an image processing model platform system environment100, in accordance with one embodiment of the present invention;

FIG. 2 depicts an image processing technology platform moduleenvironment 200, in accordance with one embodiment of the presentinvention;

FIG. 3 depicts a schematic representation 300 of an image processingmodel 301, in accordance with one embodiment of the present invention;

FIG. 4 depicts an illustrative representation 400 of a structure of animage processing model 401, in accordance with one embodiment of thepresent invention;

FIG. 5 depicts an illustrative representation 500 of user input queriescomprising a plurality of discrete input language elements, inaccordance with one embodiment of the present invention;

FIG. 6 depicts a high level process flow 600 associated with anelectronic query engine for an image processing model database, inaccordance with one embodiment of the present invention;

FIG. 7 depicts a high level process flow 700 for management of imageprocessing model database, in accordance with one embodiment of thepresent invention; and

FIG. 8 depicts a high level process flow 800 associated with a parameterarchival storage system for image processing models, in accordance withone embodiment of the present invention.

DETAILED DESCRIPTION OF EMBODIMENTS OF THE INVENTION

Embodiments of the present invention will now be described more fullyhereinafter with reference to the accompanying drawings, in which some,but not all, embodiments of the invention are shown. Indeed, theinvention may be embodied in many different forms and should not beconstrued as limited to the embodiments set forth herein; rather, theseembodiments are provided so that this disclosure will satisfy applicablelegal requirements. Like numbers refer to elements throughout. Wherepossible, any terms expressed in the singular form herein are meant toalso include the plural form and vice versa, unless explicitly statedotherwise. Also, as used herein, the term “a” and/or “an” shall mean“one or more,” even though the phrase “one or more” is also used herein.

In some embodiments, an “entity” or “enterprise” as used herein may beany institution employing information technology resources andparticularly technology infrastructure configured for large scaleprocessing of electronic files, electronic technology event data andrecords, and performing/processing associated technology activities. Insome instances, the entity's technology systems comprise multipletechnology applications across multiple distributed technology platformsfor large scale processing of technology activity files and electronicrecords. As such, the entity may be any institution, group, association,financial institution, establishment, company, union, authority or thelike, employing information technology resources.

As described herein, a “user” is an individual associated with anentity. In some embodiments, a “user” may be an employee (e.g., anassociate, a project manager, an IT specialist, a manager, anadministrator, an internal operations analyst, or the like) of theentity or enterprises affiliated with the entity, capable of operatingthe systems described herein. In some embodiments, a “user” may be anyindividual, entity or system who has a relationship with the entity,such as a customer. In other embodiments, a user may be a systemperforming one or more tasks described herein.

In the instances where the entity is a financial institution, a user maybe an individual or entity with one or more relationships affiliationsor accounts with the entity (for example, a financial institution). Insome embodiments, the user may be an entity or financial institutionemployee (e.g., an underwriter, a project manager, an IT specialist, amanager, an administrator, an internal operations analyst, bank telleror the like) capable of operating the system described herein. In someembodiments, a user may be any individual or entity who has arelationship with a customer of the entity or financial institution. Forpurposes of this invention, the term “user” and “customer” may be usedinterchangeably. A “technology resource” or “account” may be therelationship that the user has with the entity. Examples of technologyresources include a deposit account, such as a transactional account(e.g. a banking account), a savings account, an investment account, amoney market account, a time deposit, a demand deposit, a pre-paidaccount, a credit account, a non-monetary user profile that includesonly personal information associated with the user, or the like. Thetechnology resource is typically associated with and/or maintained by anentity.

As used herein, a “user interface” or “UI” may be an interface foruser-machine interaction. In some embodiments the user interfacecomprises a graphical user interface. Typically, a graphical userinterface (GUI) is a type of interface that allows users to interactwith electronic devices such as graphical icons and visual indicatorssuch as secondary notation, as opposed to using only text via thecommand line. That said, the graphical user interfaces are typicallyconfigured for audio, visual and/or textual communication. In someembodiments, the graphical user interface may include both graphicalelements and text elements. The graphical user interface is configuredto be presented on one or more display devices associated with userdevices, entity systems, processing systems and the like. In someembodiments the user interface comprises one or more of an adaptive userinterface, a graphical user interface, a kinetic user interface, atangible user interface, and/or the like, in part or in its entirety.

As used herein, “image processing models,” “deep learning models,” and“machine learning models,” (also referred to as neural-network machinelearning image processing models) as used herein refer to programs andassociated architecture of artificial neural networks, and may beemployed interchangeably. Moreover, although described in the context ofimage processing, it is understood that embodiments of the invention arealso compatible with and operate upon other deep/machine learningapplications. In particular, the electronic query engine, the imageprocessing technology platform module, the parameter archival storagesystem, image processing deep learning model regulator system, and othercomponents of the present invention are compatible with and can operateupon any deep/machine learning model, and particular on any neuralnetwork based deep/machine learning model.

Increasingly prevalent computers, mobile phones, smart devices,appliances, and other devices, require a variety of complex functionsinvolving image processing, identification, pattern recognition, 3Dimage reconstruction, visualization, modelling, etc. However, thesecomplex functions require complex processing capabilities that are notmet by conventional systems. Machine learning or deep learning modelsare required for these tasks. There is a need for improvedmachine-learning neural-network based models that have the ability tolearn and model non-linear and complex relationships, generalize andinfer unseen relationships, have the ability to handle a variety ofinputs, thereby overcoming the deficiencies of conventional systems. Thepresent invention, as illustrated by FIGS. 1-8 and as described herein,provides solutions to various problems prevalent in neural-network basedmachine-learning technology.

First, existing neural-network based machine-learning models, by theirinherent structure itself (e.g., hidden layers, non-linear nature,constant changes due to learning based on inputs provided, etc.),consist of a “black box” type structure that necessarily obscures theneural network and makes interpretability of the neural networkchallenging. For instance, it is extremely arduous and unfeasible, ifnot impossible, for a user or even another system to ascertain why orhow the neural-network arrived at a certain output, what the components(e.g., hyper parameters) of the neural network are, or what componentscaused a particular output (e.g., a defective output). Indeed,typically, neural networks themselves are not capable of identifyingtheir own hyper parameters, because hyper parameters areprovided/defined by a user during construction of the neural network.Moreover, with conventional neural-network based machine-learningtechnology, it is not possible to search the hierarchical linkedarchitectures for neural network based image processing models stored ina repository, to identify models with certain parameters. It istypically not feasible or even possible for a user to interpret each andevery element/component/function of the myriad hidden layers and othercomponents of numerous neural network based models in a repository.Embodiments of the invention alleviate these defects, e.g., as describedwith respect to FIGS. 1-8 and particularly with respect to FIGS. 6-7.Specifically, embodiments of the invention are directed to an electronicquery engine that provides a model abstraction layer over the neuralnetwork based image processing models. This model abstraction layer isstructured for interrogation, selection, mutation and construction ofthe image processing models with reduced memory, time and processingrequirement, using mere discrete input language elements typically inthe form of natural language, and without requiring the user tointerpret each and every element/component/function of the myriad hiddenlayers and other components. Moreover, the electronic query engine isalso configured for image processing tasks such as segmentation andobject detection, by generalizing model exploration and enumerationqueries from commonly conducted tasks by machine/deep learning modelers.

Second, construction, training, and optimization of conventional neuralnetwork based machine-learning models, by their very nature, areextremely time consuming and computationally expensive, and also requirelarge amounts of data for training and optimization purposes. Reducingthe time spent in training and optimization and reducing the trainingdata provided would result in inaccurate models. Typically, conventionalneural-network based machine-learning technology does not allowleveraging existing models to alleviate the foregoing time and datarequirements. Embodiments of the invention provide solutions to theseproblems, e.g., as described with respect to FIGS. 1-8 and particularlywith respect to FIGS. 6-7. Specifically, embodiments of are directed toversioning machine-learning neural-network based image processingmodels, thereby allowing a user to mutate existing machine-learningneural-network based models for new/different/additionalapplications/purposes using mere discrete input language elementstypically in the form of natural language, without requiring timeconsuming and computationally expensive construction of new models.Moreover, embodiments of the invention are also configured foridentifying and tracking mutations in hyper parameters amongst versionsof image processing models.

Third, neural network based machine-learning models typically have acomplex architecture involving numerous interconnected layers,components and functions. Storing and managing (e.g., checking-in andchecking-out, versioning, etc.) such complex neural network basedmachine-learning models and their artifacts is cumbersome, and requireslarge amounts of memory. Embodiments of the invention provide solutionsto these problems, e.g., as described with respect to FIGS. 1-8 andparticularly with respect to FIG. 8. Specifically, embodiments provide aparameter archival storage system configured for read-optimizedcompression storage of machine-learning neural-network based imageprocessing models with reduced storage. Here, the parameter archivalstorage system is configured for determining and using deltas (.e.g.,mutated/altered weights filters) in the structure of neural networkbased machine-learning models. This novel storing system is configuredfor reducing/compressing the storage requirements for a neural networkbased machine-learning model from storing complex architecture involvingnumerous interconnected layers, components and functions, to storingmere floating point bits, providing data compression in the range of100,000:1, 10,000:1, 1000:1, etc., without losing any accuracy of themodel.

FIG. 1 illustrates an image processing model platform system environment100, in accordance with some embodiments of the present invention. Asillustrated in FIG. 1, an image processing system 108 is in operativecommunication with and operatively coupled to, via a network 10, a userdevice 104, an entity server 106, a technology system 105, and an imagecapture device 180. In this way, the image processing system 108 cansend information to and receive information from the user device 104,the entity server 106, the technology system 105 and the image capturedevice 180. FIG. 1 illustrates only one example of an embodiment of thesystem environment 100, and it will be appreciated that in otherembodiments one or more of the systems, devices, or servers may becombined into a single system, device, or server, or be made up ofmultiple systems, devices, or servers. In this way, an image processingtechnology platform module 200 of the image processing system 108, isstructured for an electronic query engine for constructing a modelabstraction layer configured for selection, mutation and construction ofthe image processing models, an electronic system for versioningmachine-learning neural-network based image processing models andidentifying and tracking mutations in hyper parameters amongst versionsof image processing models, and a parameter archival storage systemconfigured for read-optimized compression storage of machine-learningneural-network based image processing models with reduced storage, byseparately storing weight filter bits, which would not be possible inthe absence of the present invention (e.g., in accordance with theprocess flows illustrated in FIGS. 6-8).

The network 101 may be a system specific distributive network receivingand distributing specific network feeds and identifying specific networkassociated triggers. The network 101 may also be a global area network(GAN), such as the Internet, a wide area network (WAN), a local areanetwork (LAN), or any other type of network or combination of networks.The network 101 may provide for wireline, wireless, or a combinationwireline and wireless communication between devices on the network 101.

In some embodiments, the user 102 may be one or more individuals orentities that may either provide static UI images (e.g., via the imagecapture device 180), e.g., for model training, request selection andcheck-out of image processing models, input queries for search andselection of models, initiate mutation of models, view displayed models,etc. As such, in some embodiments, the user 102 may be associated withthe entity and/or a financial institution.

FIG. 1 also illustrates a user system 104. The user device 104 may be,for example, a desktop personal computer, a mobile system, such as acellular phone, smart phone, personal data assistant (PDA), laptop, aserver system, another computing system and/or the like. The user device104 generally comprises a communication device 112, a processing device114, and a memory device 116. The user device 104 is typically acomputing system that is configured to enable user and deviceauthentication for access to technology event data, request constructionof UIs, receive the constructed UIs, etc. The processing device 114 isoperatively coupled to the communication device 112 and the memorydevice 116. The processing device 114 uses the communication device 112to communicate with the network 101 and other devices on the network101, such as, but not limited to, the entity server 106, the imageprocessing system 108 and the technology system 105. As such, thecommunication device 112 generally comprises a modem, server, or otherdevice for communicating with other devices on the network 101.

The user device 104 comprises computer-readable instructions 110 anddata storage 118 stored in the memory device 116, which in oneembodiment includes the computer-readable instructions 110 of a userapplication 122. In some embodiments, the image processing system 108and/or the entity system 106 are configured to cause the processingdevice 114 to execute the computer readable instructions 110, therebycausing the user device 104 to perform one or more functions describedherein, for example, via the user application 122 and the associateduser interface of the user application 122.

FIG. 1 also illustrates an image capture device 180. In someembodiments, the image capture device 180 is typically configured tocapture a 2-D image of a physical, tangible object, thereby convertingit into an electronic file/document. The image capture device 180 maybe/or may comprise, for example, a scanner, a camera, a light sensor, amagnetic reader, and/or the like. In some embodiments, the image capturedevice 180 is a part of, or is integral with the image processing system108. In some embodiments, the image capture device 180 is a part of, oris integral with the entity server 106. In some embodiments, the imagecapture device 180 is a part of, or is integral with the user device104.

As further illustrated in FIG. 1, the image processing system 108generally comprises a communication device 146, a processing device 148,and a memory device 150. As used herein, the term “processing device”generally includes circuitry used for implementing the communicationand/or logic functions of the particular system. For example, aprocessing device may include a digital signal processor device, amicroprocessor device, and various analog-to-digital converters,digital-to-analog converters, and other support circuits and/orcombinations of the foregoing. Control and signal processing functionsof the system are allocated between these processing devices accordingto their respective capabilities. The processing device, such as theprocessing device 148, typically includes functionality to operate oneor more software programs, an image processing DL Regulator system 210(having a model versioning system (MVS) module 212, an image processingDL model query command application 214, an image processing DL modelanalysis and processing system module 216, etc.), a local modelversioning system (MVS) repository 230 (having image processing DL modelmetadata 232, git repository 234, parameter archival storage 240, etc.),an image processing model control server 250 (having image processing DLmodel search component 254, Image processing DL model publishingcomponent 256, etc.), hosted model versioning system (MVS) repository(s)260, an image processing deep learning (DL) model construction system205, as well as a parameter archival storage (PAS) system 280 of theimage processing technology platform module 200 (illustrated in FIG. 2),based on computer-readable instructions thereof, which may be stored ina memory device, for example, executing computer readable instructions154 or computer-readable program code 154 (computer executableinstructions) stored in memory device 150 to perform one or morefunctions associated with an image processing technology platform module200.

The processing device 148 is operatively coupled to the communicationdevice 146 and the memory device 150. The processing device 148 uses thecommunication device 146 to communicate with the network 101 and otherdevices on the network 101, such as, but not limited to the entityserver 106, the technology system 105, the image capture device 180 andthe user system 104. As such, the communication device 146 generallycomprises a modem, server, or other device for communicating with otherdevices on the network 101.

As further illustrated in FIG. 1, the image processing system 108comprises the computer-readable instructions 154 stored in the memorydevice 150, which in one embodiment includes the computer-readableinstructions 154 of the image processing technology platform module 200.In some embodiments, the computer readable instructions 154 compriseexecutable instructions associated with the image processing DLRegulator system 210 (having the model versioning system (MVS) module212, the image processing DL model query command application 214, theimage processing DL model analysis and processing system module 216,etc.), the local model versioning system (MVS) repository 230 (havingthe image processing DL model metadata 232, git repository 234,parameter archival storage 240, etc.), the image processing modelcontrol server 250 (having image processing DL model search component254, Image processing DL model publishing component 256, etc.), thehosted model versioning system (MVS) repository(s) 260, the imageprocessing deep learning (DL) model construction system 205, and/or theparameter archival storage (PAS) system 280 of the image processingtechnology platform module 200 (e.g., as illustrated in FIG. 2), whereinthese instructions, when executed, are typically configured to cause theapplications or modules to perform/execute one or more steps describedherein (e.g., with respect to FIGS. 2-7). In some embodiments, thememory device 150 includes data storage 152 for storing data related tothe system environment, but not limited to data created and/or used bythe image processing technology platform module 200 and itscomponents/modules. The image processing technology platform module 200is further configured to perform or cause other systems and devices toperform the various steps in processing electronic records, as will bedescribed in detail later on.

As such, the processing device 148 is configured to perform some or allof the steps described throughout this disclosure, for example, byexecuting the computer readable instructions 154. In this regard, theprocessing device 148 may perform one or more steps singularly and/ortransmit control instructions that are configured to cause the imageprocessing DL Regulator system 210, the local model versioning system(MVS) repository 230, the image processing model control server 250, thehosted model versioning system (MVS) repository(s) 260, the imageprocessing deep learning (DL) model construction system 205, and/or theparameter archival storage (PAS) system 280 associated with the imageprocessing technology platform module 200 (e.g., as illustrated in FIG.2), entity server 106, user device 104, and technology system 105 and/orother systems and applications, to perform one or more steps describedthroughout this disclosure. Although various data processing steps maybe described as being performed by the image processing technologyplatform module 200 and/or its components/applications and the like insome instances herein, it is understood that the processing device 148is configured to establish operative communication channels with and/orbetween these modules and applications, and transmit controlinstructions to them, via the established channels, to cause thesemodule and applications to perform these steps.

Embodiments of the image processing system 108 may include multiplesystems, servers, computers or the like maintained by one or manyentities. FIG. 1 merely illustrates one of those systems 108 that,typically, interacts with many other similar systems to form theinformation network. In one embodiment of the invention, the imageprocessing system 108 is operated by the entity associated with theentity server 106, while in another embodiment it is operated by asecond entity that is a different or separate entity from the entityserver 106. In some embodiments, the entity server 106 may be part ofthe image processing system 108. Similarly, in some embodiments, theimage processing system 108 is part of the entity server 106. In otherembodiments, the entity server 106 is distinct from the image processingsystem 108.

In one embodiment of the image processing system 108, the memory device150 stores, but is not limited to the image processing technologyplatform module 200, as will be described later on with respect to FIG.2. In one embodiment of the invention, the image processing technologyplatform module 200 may be associated with computer-executable programcode that instructs the processing device 148 to operate the networkcommunication device 146 to perform certain communication functionsinvolving the technology system 105, the user device 104 and/or theentity server 106, as described herein. In one embodiment, thecomputer-executable program code of an application associated with theimage processing technology platform module 200 may also instruct theprocessing device 148 to perform certain logic, data processing, anddata storing functions of the application.

The processing device 148 is configured to use the communication device146 to receive data, receive requests/user queries, and the like. In theembodiment illustrated in FIG. 1 and described throughout much of thisspecification, the image processing technology platform module 200 mayperform one or more of the functions described herein, by the processingdevice 148 executing computer readable instructions 154 and/or executingcomputer readable instructions associated with one or moreapplication(s)/devices/components of the image processing technologyplatform module 200.

As illustrated in FIG. 1, the entity server 106 is connected to theimage processing system 108 and may be associated with may be associatedwith model training images, may be associated with a financialinstitution network, etc. In this way, while only one entity server 106is illustrated in FIG. 1, it is understood that multiple network systemsmay make up the system environment 100 and be connected to the network101. The entity server 106 generally comprises a communication device136, a processing device 138, and a memory device 140. The entity server106 comprises computer-readable instructions 142 stored in the memorydevice 140, which in one embodiment includes the computer-readableinstructions 142 of an institution application 144. The entity server106 may communicate with the image processing system 108. The imageprocessing system 108 may communicate with the entity server 106 via asecure connection generated for secure encrypted communications betweenthe two systems for communicating data for processing across variousapplications.

As further illustrated in FIG. 1, in some embodiments, the imageprocessing model platform system environment 100 further comprises atechnology system 105, in operative communication with the imageprocessing system 108, the entity server 106, and/or the user device104. Typically, the technology system 105 comprises a communicationdevice, a processing device and memory device with computer readableinstructions, which may be operated by the processor executing thecomputer readable instructions associated with the technology system105, as described previously. In some instances, the technology system105 is owned, operated or otherwise associated with third partyentities, while in other instances, the technology system 105 isoperated by the entity associated with the systems 108 and/or 106.Although a single external technology system 105 is illustrated, itshould be understood that, the technology system 105 may representmultiple technology servers operating in sequentially or in tandem toperform one or more data processing operations.

It is understood that the servers, systems, and devices described hereinillustrate one embodiment of the invention. It is further understoodthat one or more of the servers, systems, and devices can be combined inother embodiments and still function in the same or similar way as theembodiments described herein.

FIG. 2 illustrates the UI technology platform module environment 200 for(i) an electronic query engine for constructing a model abstractionlayer configured for selection, mutation and construction of the imageprocessing models, (ii) an electronic system for versioningmachine-learning neural-network based image processing models andidentifying and tracking mutations in hyper parameters amongst versionsof image processing models, and (iii) a parameter archival storagesystem configured for read-optimized compression storage ofmachine-learning neural-network based image processing models withreduced storage. In some embodiments, computer readable instructions154, when executed by the processing device 148 of the image processingsystem 108 (hereinafter referred to as “the system”) (depicted in FIG.1), are typically configured to cause the modules, applications, andother components of the technology platform module environment 200 toperform one or more functions as described herein. The image processingtechnology platform module 200 typically comprises an image processingDL Regulator system 210, a local model versioning system (MVS)repository 230, an image processing model control server 250, hostedmodel versioning system (MVS) repository(s) 260, an image processingdeep learning (DL) model construction system 205, and/or a parameterarchival storage (PAS) system 280, in operative communication with eachother.

In some embodiments, the term “module” as used herein may refer to afunctional assembly (e.g., packaged functional assembly) of one or moreassociated electronic components and/or one or more associatedtechnology applications, programs, and/or codes. Moreover, in someinstances, a “module” together with the constituent electroniccomponents and/or associated technology applications/programs/codes maybe independently operable and/or may form at least a part of the systemarchitecture. In some embodiments, the term “module” as used herein mayrefer to at least a section of a one or more associated technologyapplications, programs, and/or codes and/or one or more associatedelectronic components. It is also noted that machine learning and deeplearning are interchangeably utilized throughout this description.

The image processing technology platform module 200 typically comprisesthe image processing DL Regulator system 210, which is typicallyconfigured for transformation and processing of neural-network basedmachine learning models. In this regard, the image processing DLRegulator system 210 comprises a model versioning system (MVS) module212, an image processing DL model query command application 214, and animage processing DL model analysis and processing system module 216.Specifically, in some embodiments, the image processing DL Regulatorsystem 210 is a global centralized system for constructing modelabstraction layers over neural networks via the image processing DLmodel query command application 214 (also referred to as an electronicquery engine 214) which are configured for (i) versioning neural-networkbased machine learning models (also referred to as neural-networkmachine learning image processing models) via the MVS module 212, (ii)selection, mutation and construction of the neural-network based machinelearning models via the deep learning query language parser andoptimizer 215, and (iii) processing/analysis, tracking characteristics(e.g., hyper parameters), inventory of the number of models, theirperformance and statistics of the neural-network based machine learningmodels via the image processing DL model analysis and processing systemmodule 216. These model abstraction layers are constructed such thatneural-network based machine learning models can be selected, mutatedand constructed, via user input queries comprising a plurality ofdiscrete input language elements in the form of natural languagecharacter strings, which are transformed into actionable, neural-networkcompatible, machine-executable instructions by the deep learning querylanguage parser and optimizer 215.

The model versioning system (MVS) module 212, via its command line anduser interface (UI) tool components 213, in general, is structured forversioning machine-learning neural-network based image processingmodels, thereby allowing a user to mutate existing machine-learningneural-network based models for new/different/additionalapplications/purposes using mere discrete input language elementstypically in the form of natural language, without requiring timeconsuming and computationally expensive construction of new models.Moreover, embodiments of the invention are also configured foridentifying and tracking mutations in hyper parameters amongst versionsof image processing models. The command line and UI tool components 213are also structured for uploading a constructed model from a local modelversioning system (MVS) repository 230 (e.g., that is associated withthe user device 104 of a particular user) to the image processing modelcontrol server 250 (e.g., a global system associated with numeroususers) and perform the necessary transformations, as well as extractinga model from the hosted repositories 260 via the image processing modelcontrol server 250 onto the image processing DL model regulator system210 for processing and/or onto the local MVS repository 230.

Specifically, the MVS module 212 is structured for check-in andcheck-out of versioning machine-learning neural-network based imageprocessing models (e.g., those stored in the hosted MVS repository(s)260, or in the local MVS repository 230), determine and track mutationsand versions of the image processing models, highlight the mutations andvariations in hyper parameters, and also highlight whether a singlemodel has been operated upon by several users and track individual useractions thereon. The MVS module 212, via the DL model version managementenumeration and model publishing application 252 is also structured forversioning, processing and uploading models constructed by the user atthe local MVS repository 230 onto the hosted MVS repositories 260 viathe image processing model control server 250. In some embodiments, theMVS module 212 may perform one or more of these steps in response toreceiving user queries containing a plurality of discrete elements(e.g., similar to the queries (510, 520) illustrated in FIG. 5) eachcontaining natural language strings. Here, the user queries compriseoperational discrete elements (512 a, 522 a) of a model versionmanagement type. In some embodiments, such operational discrete elements(512 a, 522 a) may comprise commands such as “init” for initializing amodel at the MVS repository 260, “add” for adding the image processingmodel to be committed into the MVS repository 260, “commit” forcommuting the added image processing model, “copy” for scaffolding a newmodel from an existing model in the repository, “archive” for archivingmodels in the MVS repository 260, etc.

The image processing DL model query command application 214 (alsoreferred to as the electronic query engine 214), via its deep learningquery language parser and optimizer 215, in general, is structured forproviding a model abstraction layer over the neural network based imageprocessing models. This model abstraction layer provides an userinterface through which the user can initiate interrogation, selection,mutation and construction of the image processing models, using merediscrete input language elements typically in the form of naturallanguage, and without requiring the user to interpret each and everyelement/component/function of the myriad hidden layers and othercomponents at the hosted MVS repository(s) 260. Here, the user queriesmay be similar to the queries (510, 520) illustrated in FIG. 5,containing a plurality of discrete elements. Indeed, this naturallanguage compatible abstraction layer extends throughout the imageprocessing technology platform module 200, and is structured to becompatible with the module 200's functions described herein.Specifically, the deep learning query language parser and optimizer 215is configured for receiving and parsing user queries in the form ofdiscrete input language elements each comprising natural languagestrings, optimize the user queries, transform the discrete inputlanguage elements into neural-network compatible instructions, and feedinstructions to transform/mutate the image processing models accordinglyto the respective system/module. Here, the image processing DL modelquery command application 214 may be invoked by the MVS module 212, theimage processing model control server 250, etc., for converting receiveduser queries in the form of discrete input language elements eachcomprising natural language strings.

The image processing DL model analysis and processing system module 216,via its deep learning model wrapper 217, is structured for trackingcharacteristics of the image processing models (e.g., hyper parameters),inventory of the number of models, their performance and statistics,etc. Moreover, the deep learning model wrapper 217 is structured forinterrogation of the image processing model, typically, in response touser queries related to model exploration. Here, the user queries may besimilar to the queries (510, 520) illustrated in FIG. 5, containing aplurality of discrete elements, with the operational discrete elements(512 a, 522 a) being of the model analysis type. In some embodiments,such operational discrete elements (512 a, 522 a) may comprise commandssuch as “list” for listing models and related lineages, “desc” fordescribing a particular/selected model, “diff” for comparing multiplemodels, “eval” for evaluating the model with given input test data, etc.

Now referring to the local MVS repository 230, the local MVS repository230 is typically a local storage associated with the user device 104. Insome embodiments, the image processing model data comprises metadata,model artifacts, and hyper parameters. The metadata of the imageprocessing models constructed or trained by the user may be stored atthe image processing DL model metadata 232, while the model artifactsmay be stored at the git repository 234 with a suitable folderstructure. The hyper parameters of the image processing model may bestored in a compressed form at the parameter archival storage 240 viathe parameter archival storage (PAS) system 280, as will be describedlater on. As such, during the creation or training of the imageprocessing model, the files corresponding to the metadata, modelartifacts, and hyper parameters may be stored (e.g., temporarily) at thelocal MVS repository 230. When the user wishes the image processingmodel to be globally available, the user may check-in the model to thehosted MVS repositories 260. Here, the user may input user queriescontaining a plurality of discrete elements (e.g., similar to thequeries (510, 520) illustrated in FIG. 5) each containing naturallanguage strings. These user queries comprise operational discreteelements (512 a, 522 a) of a model version management type describedabove. In some embodiments, such operational discrete elements (512 a,522 a) may comprise commands such as “init” for initializing a model atthe MVS repository 260, “add” for adding the image processing model tobe committed into the MVS repository 260, “commit” for commuting theadded image processing model, “copy” for scaffolding a new model from anexisting model in the repository, “archive” for archiving models in theMVS repository 260, etc. Based on receiving these queries from the userdevice 104, the MVS module 212 may transmit the model files to be storedto the image processing model control server 250, which may then storethe files at the repository 260 and make the model globally availablevia the image processing DL model publishing component 256.

Moreover, the user may seek to check-out an existing model in the hostedMVS repository(s) 260, e.g., for the purposes of analyzing, viewing,comparing or mutating the model. Here, the user may input user queriescontaining a plurality of discrete elements (e.g., similar to thequeries (510, 520) illustrated in FIG. 5) each containing naturallanguage strings. These user queries comprise operational discreteelements (512 a, 522 a) of a remote interaction type comprising commandssuch as “publish” for publishing a model (e.g., a mutated model) to thesystem 210, “search” for searching/selecting models in the MVSrepository 260, “pull” for downloading model files, etc. Based onreceiving these queries from the user device 104, the MVS module 212 maytransmit instructions to the image processing model control server 250to retrieve the files via the image processing DL model search component254, and subsequently deliver the model files (e.g., afterreconstructing the model) to the user device 104.

The image processing deep learning (DL) model construction system 205 isstructured for construction and mutation of image processing models.

The parameter archival storage (PAS) system 280 is structured forread-optimized compression storage of machine-learning neural-networkbased image processing models with reduced storage. Here, the parameterarchival storage system is configured for determining and using deltas(.e.g., mutated/altered weights filters) in the structure of neuralnetwork based machine-learning models. This novel storing system isconfigured for reducing/compressing the storage requirements for aneural network based machine-learning model from storing complexarchitecture involving numerous interconnected layers, components andfunctions, to storing mere floating point bits, providing datacompression in the range of 100,000:1, 10,000:1, 1000:1, etc., withoutlosing any accuracy of the model. The functions of the PAS system 280will be described in greater detail with respect to FIG. 8 later on.

An illustrative example of the functioning of the image processingtechnology platform module 200, in accordance with some embodiments,will be described now. A neural network based deep/machine learningmodel (e.g., such as image processing model 301 of FIG. 3 having 2convolution and pooling layers, 2 Relu Activation functions and initialweights) may be constructed at the local MVS repository 230, e.g., viathe image processing DL model construction system 205. In someinstances, this model, prior to compression by the PAS system 280comprises the model hierarchical architecture which may be stored at thelocal repository 230 with the name such as “imagearch” and also all ofthe weigh parameter objects which may be stored at the local repository230 with the name such as “imagepas”. The system may then initiatetraining of the model by providing training images to the model. Here,the system typically constructs metadata for the model and stores it atimage processing DL model metadata 232, and constructs parameterizedstorage via the PAS system 280 (e.g., highly compressed storage of themodel having altered weigh parameter objects and hyperparameters duringtraining, as described by FIG. 8) and stores it at parameter archivalstorage 240, and constructs model artifacts and stores it in apredetermined folder structure in the Git Repository 234. Aftercompression via the PAS system 280, the system may construct truncatedcompressed storage components of the models by separately storingaltered weight filter bits (e.g., altered weigh parameter objects aftertraining of the model or mutation of an existing model) weigh parameterobjects with a name such as “imagepas2” and altered hyperparameters witha name such as “imagearch2” at the parameter archival storage 240 (e.g.,after discarding the rest of the components).

Next, the system may initiate transmission of the model into thecentralized hosted MVS repositories 260. The system typically performsthis in response to receiving a user input query comprising a pluralityof discrete input language elements having the operational discreteelement of “init”, such as:

“init imagenew” or

“init imagenew at REPOS_260”

In response to processing this user input via the DL model query commandapplication 214, the system transforms the natural language into machineinstructions and invokes an “init” function (e.g., stored in thecomputer readable instructions 154) and constructs a repository with thename “imagenew” in the centralized hosted MVS repositories 260.

The system may then receive user input queries comprising a plurality ofdiscrete input language elements having the operational discrete elementof “add”, such as:

“add imagepas2” or “add imagepas2 to imagenew” and

“add imagearch2” or “add imagearch2 to imagenew”

In response to processing these user inputs via the DL model querycommand application 214, the system invokes an “add” function (e.g.,stored in the computer readable instructions 154) and transmits themodels files (e.g., of the image processing model 301) to the hosted MVSrepositories 260, via the server 250. The user may then commit the filesof the model in the hosted MVS repositories 260 via a query having anoperational discrete element of “commit”, whereupon the system may makethe model available globally, and which may be access through userinputs DL regulator system 210. Here, the user may also publish themodel to DL regulator system 210 using a query having an operationaldiscrete element of “publish.” The pre-compression files andarchitecture stored at the local MVS repository 230 may then bediscarded/deleted.

Now, if the user (or another user) seeks to view, operate, train ormutate a particular model out of one or more image processing modelsstored in the hosted MVS repositories 260, the user may input a queryhaving an operational discrete element of “list.” In response toprocessing this user input via the DL model query command application214, the system may display a list of the one or more models stored atthe hosted MVS repositories 260 on the user interface of the userdevice. The user may also search for a particular model using a queryhaving an operational discrete element of “search.”

In the user seeks to determine attributes of a particular model (e.g.,model 301 stored as “imagenew”), the user may input a query having anoperational discrete element of “desc,” such as “desc imagenew.” Inresponse to processing this user input via the DL model query commandapplication 214, the system may display attributes of the model on theuser interface of the user device such as descriptions, comprehensivecharacteristics, type of neural network being used, stages, etc.

If the user seeks to evaluate the accuracy of the model, the user mayinput a query having an operational discrete element of “eval.” Forinstance, the user may input a query such as “eval imagenew fortestcase1.” In response to processing this user input via the DL modelquery command application 214, the system may extract “testcase1” filesfrom the respective storage location and utilize them to performaccuracy testing of the model (e.g., after reconstructing the model fromthe compressed storage), and display the results and progress.

If the user seeks to replicate the model, the user may input a queryhaving an operational discrete element of “copy.” In response toprocessing this user input via the DL model query command application214, the system may scaffold a new model accordingly. The user may thenproceed to mutate this new model. If the user seeks to compare the modelwith another, the user may input a query having an operational discreteelement of “diff.” In response to processing this user input via the DLmodel query command application 214, the system may initiate comparisonand tracking of the changes.

If the user seeks to download the model for remote interaction, the usermay input a query having an operational discrete element of “pull.”

FIG. 3 illustrates a schematic representation 300 of an image processingmodel 301, in accordance with one embodiment of the present invention.The image processing model may receive an image input 312 via imageinput batch 310. As illustrated, the image processing model 301 maycomprise a plurality of hidden layers 320. These hidden layers 320typically comprise alternating non-linear activation layers (322, 332)(e.g., convolution neural network layers) and pooling neural networklayers (328, 338). Moreover, the each of the non-linear activationlayers (322, 332) may further comprise a convolution component (324,334) and an activation function (326, 336), such as a rectified linearunit (ReLU), as illustrated. Based on processing the image input 312,the image processing model 301 may construct classification layers 320,such as flatten 362, fully connected layer 364, and Softmax 366.

FIG. 4 depicts an illustrative representation 400 of a structure 401 ofthe image processing model 301, in accordance with one embodiment of thepresent invention. Specifically, FIG. 4 illustrates a schematicrepresentation of the storage of data elements associated with thehierarchical architecture 401 of the image processing model 301 of FIG.3. Specifically, the storage structure of the model may comprise ahierarchical linked architecture comprising a plurality of datastructures 402-490. The data structures may comprise a pixel coordinatesassociated with the image input 312 and the corresponding weights in theform of floating point decimals.

The functioning of the image processing model 301 will now be describedin conjunction with FIGS. 3 and 4. Here, the system may seek toconstruct and train the model 301 to identify vehicles in an image. Thesystem may utilize a batch of images 312 having depictions of variousvehicles and other non-vehicular depictions for the purposes of trainingand testing the model 301. Each image 312 is a matrix/collection ofpixels arranged in rows and columns. Each pixel is associated with (i)pixel coordinates (location coordinates in the form of row number andcolumn number) and (ii) color value weights (red, blue, green values,i.e., RBG values). In other words, a pixel having a particularcoordinate (n (row), m (col)) comprises an associated RBG color value.

Certain systems may employ linear models for analyzing the image.However, in the image 312 there is no linear correlation between thepixel coordinate and the object required to the identified/recognize. Inother words, there is no correlation that with an increase in x axisthere would be more objects (e.g., vehicles), or that with a decrease inY axis we would have less objects (e.g., pedestrians). The objects canappear anywhere in the image depending on the image frame, therebyexhibiting a non-linear relationship with the rows and columns of thepixels in the image.

The linear output of pixel coordinates' rows and columns need to beconverted to non-linear outputs associated with location of the objectbeing identified. To overcome this, the system may construct a pluralityof non-linear activation layers (322, 332) having convolution components(324, 334) and activation functions (326, 336), such as a rectifiedlinear unit (RELU). The activation functions (326, 336) typically arenon-linear activation functions comprising Sigmoid, TanH, ArcTan,ArSinH, ElliotSig, Softsign, Inverse square root unit (ISRU), Inversesquare root linear unit (ISRLU), Square Nonlinearity (SQNL), Rectifiedlinear unit (ReLU), Bipolar rectified linear unit (BReLU), Leakyrectified linear unit (Leaky ReLU), Parameteric rectified linear unit(PReLU), Randomized leaky rectified linear unit (RReLU), Exponentiallinear unit (ELU), Scaled exponential linear unit (SELU), S-shapedrectified linear activation unit (SReLU), Adaptive piecewise linear(APL), SoftPlus, Bent identity, Sigmoid Linear Unit (SiLU),SoftExponential, SoftExponential, Soft Clipping, Soft Clipping, Gaussianactivation function, and/or the like.

At the first non-linear activation layer 322, the convolution component324 analyzes the image to identify major/key features, i.e.,primary/principal/fundamental features/objects of the image. The systemmay initialize the image by assigning floating point decimal weights tothe each of the pixel coordinates for locating major/key features, i.e.,primary/principal/fundamental features/objects of the image. E.g., theweights may be assigned such that higher weights or weights in aparticular range may indicate presence of a primary feature/object(e.g., an object to be identified, a collection or objects to beidentified, other objects, etc.) at the corresponding pixel coordinate,thereby forming data structures 402-406. Although three data structures402-406 are illustrated in FIG. 4 for ease of depiction, it isunderstood that each of the hundreds or thousands of pixels in the image312 is assigned a floating point decimal weight, thereby forming afloating point array of hundreds or thousands of data structures. Theactivation function 326 such as a ReLU function 420 then processes theimage in conjunction with the data structures 402-406 to identify thepixels associates with key features to be identified and extracts theassociated data structures 432. Again, it is noted that although asingle data structure 432 is illustrated for ease of depiction, theremay be numerous such data structures associated with key features.

Next, the system constructs a pooling layer 328 that is structured toeliminate/remove rotational variants in the image (e.g., for thepurposes of identifying a vehicle, even if the vehicle is depicted astilted or up-side down or as a reflection in the image, instead of beingdepicted straight upright with wheels on the ground). Here the systemmay employ a hyperbolic functions such as TanH. It is noted that, asthese hidden layers 320 are successively built, a corresponding complexhierarchical geometrical data structure architecture associated with themodel is created and stored as depicted by FIG. 4.

Similar to the first non-linear activation layer 322 and the firstpooling layer 328, the system may construct a second non-linearactivation layer 332 (having a convolution layer component 324 and anactivation fiction 336 of Function 460) and a second pooling layer 338,and the corresponding geometric hierarchy levels of the data structures452-456 and 460 are appended (i.e., constructed and linked) to those ofthe first non-linear activation layer 322 and the first pooling layer328 as illustrated in FIG. 4. Many additional non-linear activationlayers and pooling layers may successively constructed, and theircorresponding geometric hierarchy levels of the data structures areconstructed and appended to the stored architecture 401.

Next, the system constructs classification layers 360. The system mayfirst construct a flatten layer 362. Here, the system converts themulti-dimensional array of key features received from the precedingpooling layer into a single column array having all of the correspondingfloating point weights. This is then fed into a constructed fullyconnected (FC) layer 364 having numerous neurons. Each of the neuronscomprises a weight. In the fully connected layer 364, each of the neuronweighs is multiplied with the corresponding floating point weights,thereby forming data structures 472 and 474. The system may thenconstruct a classification program/algorithm component 366 such asSoftmax or Softmax cross entropy 480. The classificationprogram/algorithm component 366 is structured to analyzed the productsof the multiplication in the fully connected layer 364 to categorizedthe identified features into various categories of vehicles (e.g.,C1-bicycle, C2-car, C3-Truck, C4-bus, etc.), and construct acorresponding data structure 490 indicating the final identifiedcategorized object—e.g., a car.

In this way, as depicted by FIGS. 3-4, the model 301 in the form of acomplex hierarchical geometrical data structure architecture 401 isconstructed and stored having (i) hierarchical sequential arrangementframework of the layer components and (ii) corresponding weightcomponents (in the form of kernels of ((Pixel Coordinates), FloatingPoint Weights) such as 402-4706, 432, . . . and corresponding operativefunctions such as 420, 440, 460, 480, . . . ). It is noted that storingthis complex, multilayered architecture and correspondinghundreds/thousands of data structures for each and every model of themyriad of models in the repository 260, requires immense memory andprocessing power. To address this problem, the present inventionprovides a parameter archival storage system for compressions storage ofthe model as will be described with respect to FIG. 8, later on.

Moreover, the system may then compare the identified categorized objectwith the ground truth to determine if the identification by the modelwas accurate. If the identification was not accurate, the system thenmodify the assigned weights, mutate the activation functions, etc.untill the desired accuracy is achieved.

FIG. 5, illustrates an illustrative representation 500 of user inputqueries comprising a plurality of discrete input language elements, inaccordance with one embodiment of the present invention. As describedwith respect to FIG. 2, the user may input user queries (510, 520)containing a plurality of discrete elements (512 a-512 e, 522 a-522 f)each containing natural language strings. Here, the user queriescomprise operational discrete elements (512 a, 522 a) of a model versionmanagement type such as “init” for initializing a model at the MVSrepository 260, “add” for adding the image processing model to becommitted into the MVS repository 260, “commit” for commuting the addedimage processing model, “copy” for scaffolding a new model from anexisting model in the repository, “archive” for archiving models in theMVS repository 260, etc. The operational discrete elements (512 a, 522a) may also be of a model analysis type, such as “list” for listingmodels and related lineages, “desc” for describing a particular/selectedmodel, “diff” for comparing multiple models, “eval” for evaluating themodel with given input test data, etc. The operational discrete elements(512 a, 522 a) may also be of a remote interaction type comprisingcommands such as “publish” for publishing a model (e.g., a mutatedmodel) to the system 210, “search” for searching/selecting models in theMVS repository 260, “pull” for downloading model files, etc.

As a non limiting example of the user query 510, the user query may takethe form of:

“select m1 from PAS_REPOS where m1.activation==‘return”’

Here, the operational discrete element 512 a indicating the action to beperformed is “select,” the identifier discrete element 512 c indicatingthe location/name of the model/program comprises “m1”, the operatordiscrete element 512 b indicating the preposition type relationalcontext is “from”, a keyword discrete element 512 d indicating thesource location is a parametrized storage location of “PAS_REPOS”, whilethe conditional discrete element 512 e indicating the condition forperforming the task associated with the operational discrete element 512a is “where m1.activation==‘relu’”. In other words, the query isassociated with searching the PAS_REPOS location of the hosted MVSrepositories 260 to select a model with the name m1 which comprises aReLU type activation function.

As yet another non limiting example of the user query 510, the userquery may take the form of:

“select m1 where m1.name like “densenet %” and m1.creationtime>“2019-01-01” and m1[“conv[1, 3, 5]”].next POOL(“Average”)”

Here, the operational discrete element 512 a indicating the action to beperformed is “select,” the identifier discrete element 512 c indicatingthe location/name of the model/program comprises “m1”, while a firstconditional discrete element 512 e indicating the requirement forperforming the task associated with the operational discrete element 512a is “where m1.name like “densenet %””, a second conditional discreteelement specifying time of creations of “m1.creation_time>“2019-01-01,”and a third conditional discrete element specifying the type ofconvolution layer required of “m1[“conv[1, 3, 5]”].nextPOOL(“Average”)”. In other words, the query is associated with searchingthe MVS repositories 260 to select a model m1 which is constructed usingdensenet type image network, which was created after 2019-01-01, andwhich has a convolution layer with a single channel, a resolution of 3×5and which has average pooling.

The system may receive or construct such queries, and after processingthem via the DL Regulator system 210, extract the model(s) thatcorresponds to the query.

As a non limiting example of the user query 520, the user query may takethe form of:

“construct m2 from m1 where m1.activation==‘relu’ mutatem1[“relu*($1)”].=tanh(“relu$1”)”

Here, the operational discrete element 522 a indicating the action to beperformed is “construct,” the identifier discrete element 522 cindicating the location/name of the new model/program comprises “m2”,the operator discrete element 522 b indicating the preposition typerelational context is “from”, a keyword discrete element 522 dindicating the existing model is “m1”, while the conditional discreteelement 522 e indicating the condition for performing the taskassociated with the operational discrete element 522 a is “wherem1.activation==‘relu’”, and the mutation discrete element 522 findicating the mutation to be performed is “mutatem1[“relu*($1)”].=tanh(“relu$1”)”. In other words, based on processingthis query the system extracts a model m1 having a ReLU type activationfunction, and constructs a new model m2 by mutating (e.g., byoverwriting, replacing, transforming, etc.) the model m1, by mutatingthe ReLU type activation function to a Tanh type.

As another non limiting example of the user query 520, the user querymay take the form of:

“construct m2 from m1 where m1.m=name like “alexnet−avgv1%,” andm1[“conv*($1)”].next has POOL(“AVG”) mutatem1[“conv*($1)”].insert=RELU(“relu$1”)”

Here, the operational discrete element 522 a indicating the action to beperformed is “construct,” the identifier discrete element 522 cindicating the location/name of the new model/program comprises “m2”,the operator discrete element 522 b indicating the preposition typerelational context is “from”, a keyword discrete element 522 dindicating the existing model is “m1”, while a first conditionaldiscrete element 522 e indicating the condition for performing the taskassociated with the operational discrete element 522 a is “wherem1.m=name like “alexnet−avgv1%”, a second conditional discrete elementindicating “m1[“conv*($1)”].next has POOL(“AVG”)” and the mutationdiscrete element 522 f indicating the mutation to be performed is“mutate m1[“conv*($1)”].insert=RELU(“relu$1”)”. In other words, based onprocessing this query the system extracts a model m1 constructed inalexnet type image network and having a convolution layer with averagepooling, and constructs a new model m2 by mutating the model m1, byinserting a ReLU type activation function at a particular convolutionlayer conv*($1).

FIG. 6, illustrates a high level process flow 600 for an electronicquery engine (also referred to as an image processing DL model querycommand application 214) for an image processing model database, inaccordance with one embodiment of the present invention. Here, thesystem is configured for constructing a model abstraction layer formachine-learning neural-network based image processing models configuredfor selection, mutation and construction of the image processing models.As discussed previously, existing neural-network based machine-learningmodels, by their inherent structure itself (e.g., hidden layers,non-linear nature, constant changes due to learning based on inputsprovided, etc.), consist of a “black box” type structure thatnecessarily obscures the neural network and makes interpretability ofthe neural network challenging. For instance, it is extremely arduousand unfeasible, if not impossible, for a user or even another system toascertain why or how the neural-network arrived at a certain output,what the components (e.g., hyper parameters) of the neural network are,or what components caused a particular output (e.g., a defectiveoutput). Moreover, with conventional neural-network basedmachine-learning technology, it is not possible to search thehierarchical linked architectures for neural network based imageprocessing models stored in a repository, to identify models withcertain parameters. Construction, training, and optimization ofconventional neural network based machine-learning models, by their verynature, are extremely time consuming and computationally expensive, andalso require large amounts of data for training and optimizationpurposes. Reducing the time spent in training and optimization andreducing the training data provided would result in inaccurate models.Typically, conventional neural-network based machine-learning technologydoes not allow leveraging existing models to alleviate the foregoingtime and data requirements. The electronic query engine of the presentinvention provides a model abstraction layer over the neural networkbased image processing models. This model abstraction layer isstructured for interrogation, selection, mutation and construction ofthe image processing models with reduced memory, time and processingrequirement, using mere discrete input language elements typically inthe form of natural language, and without requiring the user tointerpret each and every element/component/function of the myriad hiddenlayers and other components. Moreover, the electronic query engine isalso configured for image processing tasks such as segmentation andobject detection, by generalizing model exploration and enumerationqueries from commonly conducted tasks by machine/deep learning modelers.In the embodiments described herein, the electronic query engine is alsostructured for mutating exiting models to construct new models.

First, at block 602, the system may receive, from a user device, a firstuser input query comprising a first plurality of discrete input languageelements. As discussed previously, each of the first plurality ofdiscrete input language elements comprises a character string, asdescribed with respect to FIGS. 2 and 5. Each of the character stringsare typically in natural speech/language.

In response, the system may parse the first user input query to identifyat least (i) a first operational type discrete element of the firstplurality of discrete input language elements, and (ii) a firstconditional type discrete element of the first plurality of discreteinput language elements, as depicted by block 604. As discussedpreviously, the system may identify a model version management typeoperational discrete element such as “init” for initializing a model atthe MVS repository 260, “add” for adding the image processing model tobe committed into the MVS repository 260, “commit” for commuting theadded image processing model, “copy” for scaffolding a new model from anexisting model in the repository, “archive” for archiving models in theMVS repository 260, etc. The system may identify a model analysis typeoperational discrete element, such as “list” for listing models andrelated lineages, “desc” for describing a particular/selected model,“diff” for comparing multiple models, “eval” for evaluating the modelwith given input test data, etc. The system may identify operationaldiscrete elements of a remote interaction type comprising commands suchas “publish” for publishing a model (e.g., a mutated model) to thesystem 210, “search” for searching/selecting models in the MVSrepository 260, “pull” for downloading model files, etc. The system mayfurther identify conditional type discrete elements which describe therequirements for performing the actions of the operational discreteelements, such as “when . . . ”, “if . . . ”, “where . . . ” etc., asdescribed with respect to FIG. 5.

Next, the system may determine that the first operational-type discreteelement is associated with construction of a new second image processingmodel by transforming a first image processing model from the at leastone hosted model versioning system repository. As non limiting examplesof the user input query, the user query may take the form of “constructm2 from m1 where m1.activation==‘relu’ mutatem1[“relu*($1)”].=tanh(“relu$1”)” or “construct m2 from m1 wherem1.m=name like “alexnet−avgv1%” and m1[“conv*($1)”].next has POOL(“AVG”)mutate m1[“conv*($1)”].insert=RELU(“relu$1”)”, etc. Here, the system maydetermine that the first operational-type discrete element “construct”is associated with construction of a new second image processing model(“m2”) by transforming/mutating a first image processing model (“m1”)from the at least one hosted model versioning system repository.

Subsequently, the system may extract the first image processing model ofthe one or more image processing models based on determining that atleast one of a plurality of first convolutional neural network layers ofthe first image processing model is associated with at least the firstconditional type discrete element, as illustrated by block 606. As anexample, for the user input query of “construct m2 from m1 wherem1.activation==‘relu’ mutate m1[“relu*($1)”].=tanh(“relu$1”)”, thesystem may extract the first image processing model “m1” of the one ormore image processing models based on determining that at least one of aplurality of first convolutional neural network layers of the firstimage processing model “m1” is associated with at least the firstconditional type discrete element of “activation==‘relu’”. In otherwords, the system may extract the first image processing model “m1” ofthe one or more image processing models based on determining that atleast one of a plurality of first convolutional neural network layers ofthe first image processing model “m1” is associated with ReLU typeactivation function. As another example of the user input query of“construct m2 from m1 where m1.activation==‘relu’ mutatem1[“relu*($1)”].=tanh(“relu$1”)” or “construct m2 from m1 wherem1.m=name like “alexnet−avgv1%” and m1[“conv*($1)”].next has POOL(“AVG”)mutate m1[“conv*($1)”].insert=RELU(“relu$1”)”, the system may extractthe first image processing model “m1” of the one or more imageprocessing models based on determining that at least one of a pluralityof first convolutional neural network layers of the first imageprocessing model “m1” is associated with ReLU type activation function.In some embodiments, extracting the first image processing model furthercomprises reconstructing the model from compressed parameterized storageas described previously and below. In some embodiments, the first imageprocessing model may comprise a structure similar to the imageprocessing model 301.

Next, at block 608, the system may identify a first mutation typediscrete element of the first plurality of discrete input languageelements in the first user input query. As non limiting examples of theuser input query, the user query may take the form of “construct m2 fromm1 where m1.activation==‘relu’ mutate m1[“relu*($1)”].=tanh(“relu$1”)”or “construct m2 from m1 where m1.m=name like “alexnet−avgv1%” andm1[“conv*($1)”].next has POOL(“AVG”) mutatem1[“conv*($1)”].insert=RELU(“relu$1”)”, etc. Here, the system maydetermine that the first mutation type discrete element “mutate . . . ”is associated with mutation/transformation of a new second imageprocessing model (“m2”) by transforming/mutating a first imageprocessing model (“m1”).

The system may then construct the second image processing model bymutating the first image processing model at block 610. Here, the systemmay first construct a first mutant neural network layer componentassociated with the first mutation type discrete element. Next, thesystem may construct the second image processing model by embedding thefirst mutant neural network layer component at an original convolutionalneural network layer of the plurality of first convolutional neuralnetwork layers of the first image processing model.

In some embodiments, the system constructs the first mutant neuralnetwork layer component by altering an original activation function.Here, the system may determine that the first mutation type discreteelement is associated with altering a first activation functioncomponent of the first image processing model. Next, the systemconstructs the new activation function component comprising a rectifiedlinear unit function, a TanH function, a Softmax function, a Maxoutfunction, an inverse square function, or any of the activation functionsdescribed herein. The system may then remove the first activationfunction component from the original convolutional neural network layerof the first image processing model, and insert the constructed newactivation function component at the original convolutional neuralnetwork layer of the first image processing model.

As an example, for the user input query of “construct m2 from m1 wherem1.activation==‘relu’ mutate m1[“relu*($1)”].=tanh(“relu$1”)”, thesystem may first construct a first mutant neural network layer componenthaving a tanH function. Next, the system may construct the second imageprocessing model by embedding the first mutant neural network layercomponent having a tanH function at an original convolutional neuralnetwork layer having a ReLU activation function, thereby replacing theconvolutional neural network layer having a ReLU activation functionwith the mutant neural network layer component having a tanH function.Here, the system further construct the linkages in the modelhierarchical architecture framework (similar to the architectureframework of FIG. 4) between the mutant neural network layer and theother layers.

In some embodiments, the system constructs the first mutant neuralnetwork layer component by constructing a new activation function. Herethe system may determine that the first mutation type discrete elementis associated with a new activation function component. Next, the systemconstructs the new activation function component comprising a rectifiedlinear unit function, a TanH function, a Softmax function, a Maxoutfunction, an inverse square function, or any of the activation functionsdescribed herein. The system may then insert the constructed newactivation function component at the original convolutional neuralnetwork layer of the first image processing model.

As another example, for the user input query of “construct m2 from m1where m1.m=name like “alexnet−avgv1%” and m1[“conv*($1)”].next hasPOOL(“AVG”) mutate m1[“conv*($1)”].insert=RELU(“relu$1”)”, the systemmay first construct a first mutant neural network layer component havingReLU function. Next, the system may construct the second imageprocessing model by embedding the first mutant neural network layercomponent having the ReLU function at an original convolutional neuralnetwork layer of the first model, thereby inserting the convolutionalneural network layer having a ReLU activation function at location(before a particular layer, after a particular layer, and/or in-betweentwo layers) described by the query. Here, the system further constructthe linkages in the model hierarchical architecture framework (similarto the architecture framework of FIG. 4) between the mutant neuralnetwork layer and the other layers.

In some embodiments, the second image processing model comprises aplurality of second convolution neural network layers, similar to themodel 301 described previously. These layers may comprise a plurality ofconvolution neural network layers, a plurality of pooling neural networklayers, arranged to alternate between the plurality of convolutionneural network layers, and a plurality of activation functions (e.g., atleast one rectified linear unit (ReLU) type activation function),similar to those described with respect to FIGS. 3-4. Indeed, at leastone of the layers of the constructed image processing model is theembedded mutant neural network layer described above.

As such, for constructing the second image processing model by embeddingthe first mutant neural network layer component at an originalconvolutional neural network layer of the plurality of firstconvolutional neural network layers of the first image processing model,the system may further construct a hierarchical linked architecture forthe second image processing model. As discussed previously, thehierarchical linked architecture for the second image processing modelcomprises a sequential linked arrangement of a plurality of secondconvolution neural network layers associated with the second imageprocessing model, as illustrated by FIG. 4. Moreover, the system mayfurther construct the weigh parameter objects (i.e., floating pointweights as described with respect to FIG. 4) associated with theplurality of second convolution neural network layers of the secondimage processing model. Typically, the weigh parameter objects areconstructed such that the second image processing model can bereconstructed from the weigh parameter objects.

Finally, at block 612, the system may present a graphical representationcomprising the second image processing model on a display device of theuser device. Here, the system may present a representation similar tothat of FIG. 3 and/or a representation similar to that of FIG. 4.

The unique compressed parameter archival storage (also referred to asparametrized storage) of the second image processing model will now bedescribed. The system may first map the first mutant neural networklayer component with the original convolutional neural network layer ofthe first image processing model. Here, the system may first constructand store the first mutant neural network layer component at arepository memory location. The system may then link the first mutantneural network layer component with the first image processing model atthe original convolutional neural network layer location. The system maythen discard the hierarchical linked architecture of the second imageprocessing model. Hence, the second image processing model can be storedin a compressed parameterized manner by merely storing (i) the weighparameter objects (floating point weights), and (ii) first mutant neuralnetwork layer component, at the hosted MVS repositories 260.

Specifically, the second image processing model can be stored in acompressed parameterized manner by merely storing the (i) only the weighparameter objects (floating point weights) of the second imageprocessing model that differ from that of the first image processingmodel, and (ii) first mutant neural network layer component that islinked/mapped to the first image processing model, instead of the entirehierarchical linked architecture framework of the second imageprocessing model. Here, the system may process/analyze a first pluralityof weights associated with the plurality of first convolutional neuralnetwork layers of the first image processing model and a correspondingsecond plurality of weights associated with the plurality of secondconvolution neural network layers associated with the second imageprocessing model. The system may then determine altered weights in thesecond plurality of weights that deviate from the corresponding firstplurality of weights. The system may then map the altered weights in thesecond plurality of weights with the corresponding first plurality ofweights and the corresponding plurality of first convolutional neuralnetwork layers. Indeed, here the constructed weigh parameter objects forthe second image processing model are the only altered weights (the restare discarded).

This novel storing system is configured for reducing/compressing thestorage requirements for a neural network based machine-learning modelfrom storing complex architecture involving numerous interconnectedlayers, components and functions, to storing mere floating point bits,providing data compression in the range of 100,000:1, 10,000:1, 1000:1,etc., without losing any accuracy of the model.

Later on, if the second image processing model is required to bereconstructed from the compressed parameterized storage, for instance,in response to receiving a second user input query comprising a secondplurality of discrete input language elements (e.g., “pull m2 fromPAS_REPOS” or “eval m2 from PAS_REPOS”) from the user, the system maydynamically and in real time, reconstruct the model as follows. First,similar to the parsing of the user input queries described previously,the system may identify at least (i) a second operational type discreteelement (e.g., “pull” or “eval”) and (ii) a second conditional typediscrete element (e.g., “m2 from PAS_REPOS”) in the second plurality ofdiscrete input language elements of second user input query, anddetermine that the second user input query is associated with selectionof the constructed second image processing model for analysis. Thesystem then extracts (i) the previously stored weigh parameter objectsand (ii) the first mutant neural network layer component associated withthe second image processing model. The system may then determine thatthe first mutant neural network layer component is linked/mapped to thefirst image processing model. In response, the system may extract atemporary copy of the first image processing model. The system may thendynamically reconstruct the second image processing model by mapping (i)the stored weigh parameter objects with the respective layers of thefirst model and (ii) the first mutant neural network layer componentwith an original hierarchical linked architecture of the first imageprocessing model. The system may then present this dynamicallyreconstructed second image processing model on the display device of theuser device.

FIG. 7, illustrates a high level process flow 700 for management ofimage processing model database, in accordance with one embodiment ofthe present invention. The system is configured for versioningmachine-learning neural-network based image processing models andidentifying and tracking mutations in hyper parameters amongst versionsof image processing models. As discussed previously, existingneural-network based machine-learning models, by their inherentstructure itself (e.g., hidden layers, non-linear nature, constantchanges due to learning based on inputs provided, etc.), consist of a“black box” type structure that necessarily obscures the neural networkand makes interpretability of the neural network challenging. Forinstance, it is extremely arduous and unfeasible, if not impossible, fora user or even another system to ascertain why or how the neural-networkarrived at a certain output, what the components (e.g., hyperparameters) of the neural network are, or what components caused aparticular output (e.g., a defective output). Moreover, conventionalneural-network based machine-learning technology requires models to beconstructed from scratch. Conventional neural-network basedmachine-learning technology does not allow for constructing new modelsby mutating existing models. Moreover, typically, neural networksthemselves are not capable of identifying their own hyper parameters,because hyper parameters are provided/defined by a user duringconstruction of the neural network. The electronic query engine of thepresent invention provides a model abstraction layer over the neuralnetwork based image processing models. This model abstraction layer isstructured for interrogation, selection, mutation and construction ofthe image processing models with reduced memory, time and processingrequirement, using mere discrete input language elements typically inthe form of natural language, and without requiring the user tointerpret each and every element/component/function of the myriad hiddenlayers and other components. Moreover, the electronic query engine isalso configured for image processing tasks such as segmentation andobject detection, by generalizing model exploration and enumerationqueries from commonly conducted tasks by machine/deep learning modelers.

First at block 702 the system may receive a first user input associatedwith check-out of a first image processing model from the at least onehosted model versioning system repository from a user device. Here,“check-out” may refer to extracting the model (i.e., checking-out) fromthe MVS repository(s) 260 to the local repository 230 or the user device104. Here, the user may seek to check-out an existing first imageprocessing model in the hosted MVS repository(s) 260, e.g., for thepurposes of mutating the model. Here, the user may input user queriescontaining a plurality of discrete elements (e.g., similar to thequeries (510, 520) illustrated in FIG. 5) each containing naturallanguage strings. These user queries comprise operational discreteelements (512 a, 522 a) for extracting the model (i.e., checking-out)from the MVS repository(s) 260 to the local repository 230/user device104, comprising commands such as: “search” for searching/selectingmodels in the MVS repository 260 to locate/identify the desired firstimage processing model, “pull” for downloading model files, etc. Basedon receiving these queries from the user device 104, the MVS module 212may transmit instructions to the image processing model control server250 to retrieve the files via the image processing DL model searchcomponent 254, and subsequently deliver the model files (e.g., afterdynamically reconstructing the model) to the user device 104. The systemmay further determine that the stored files of the first imageprocessing model are compressed parameterized storage. In this instance,the system may dynamically reconstruct the first image processing modeland present it on the user device's interface. In this manner, thesystem may extract the first image processing model of the one or moreimage processing models.

Here, as discussed, the first image processing model is associated witha first plurality of hyper parameters associated with: (i) a number ofconvolution neural network layers, i.e., a first plurality of firstconvolutional neural network layers, (ii) a first activation function,(iii) number of first neurons in the plurality of first convolutionalneural network layers, (iv) first loss function, (v) firstregularization component, (vi) first learning rate component of thefirst image processing model, (vii) type of optimization function,and/or other hyperparameters. The system may then extract the firstimage processing model of the one or more image processing models. It isnoted that in machine learning, a hyperparameter is a parameter whosevalue is set before the learning process begins. By contrast, the valuesof other parameters may be derived by the model via training.

Each image processing model (also referred to as neural-network/machinelearning image processing models) typically comprises an input and anoutput layer (e.g., softmax layer), as well as multiple hidden layers,as illustrated by FIG. 3. As described previously, the hidden layers ofa convolutional neural network (CNN) typically consist of a series ofconvolutional layers that convolve with a multiplication or other dotproduct. The activation function may be a ReLU type layer, and issubsequently followed by additional convolutions such as pooling layers,fully connected layers and normalization layers, referred to as hiddenlayers because their inputs and outputs are masked by the activationfunction and final convolution. As such, in some embodiments, the numberof convolution neural network layers for a model may refer to the numberof hidden layers in the model. In other embodiments, the number ofconvolution neural network layers for a model may refer to the totalnumber of layers in the model.

As described with respect to FIG. 3, each image processing model alsotypically comprises at least one activation function (e.g., activationfunctions (326, 336), such as a rectified linear unit (ReLU) typeactivation function). In some embodiments, the activation function of anode or a layer defines the output of that node given an input or set ofinputs. The activation functions typically are non-linear activationfunctions comprising Sigmoid, TanH, ArcTan, ArSinH, ElliotSig, Softsign,Inverse square root unit (ISRU), Inverse square root linear unit(ISRLU), Square Nonlinearity (SQNL), Rectified linear unit (ReLU),Bipolar rectified linear unit (BReLU), Leaky rectified linear unit(Leaky ReLU), Parameteric rectified linear unit (PReLU), Randomizedleaky rectified linear unit (RReLU), Exponential linear unit (ELU),Scaled exponential linear unit (SELU), S-shaped rectified linearactivation unit (SReLU), Adaptive piecewise linear (APL), SoftPlus, Bentidentity, Sigmoid Linear Unit (SiLU), SoftExponential, SoftExponential,Soft Clipping, Soft Clipping, Gaussian activation function, and/or thelike.

Typically, neurons are elementary units of an image processing model.Depending on the model used they may be called a semi-linear unit, Nvneuron, binary neuron, linear threshold function, or McCulloch-Pitts(MCP) neuron. Each layer of the image processing model typicallycomprises a predetermined number of neurons. The artificial neuronreceives one or more inputs (e.g., representing excitatory postsynapticpotentials and inhibitory postsynaptic potentials at neural dendrites)and sums them to produce an output (or activation, representing aneuron's action potential which is transmitted along its axon).Typically, each input is separately weighted, and the sum is passedthrough a non-linear function such as an activation function, also knownas a transfer function. The activation function of a neuron is chosen tohave a number of properties which either enhance or simplify the networkcontaining the neuron. Such activation functions may include stepfunctions, linear combination functions, sigmoid function, rectifierfunctions (e.g., ReLU), etc.

Moreover, each image processing model typically comprises one or moreloss functions. In machine-learning image processing and classification,minimized objective functions or loss functions may represent how wellthe program predicts the expected outcome in comparison with the groundtruth, i.e., the cost/value of inaccuracy of predictions (problems ofidentifying which category a particular image belongs to). These lossfunctions help the machine-learning program learn fromincorrect/inaccurate outputs. Loss functions may be of various typessuch as classification loss function type, regression loss functiontype, etc., such as Softmax loss functions, Sigmoid loss functions etc.

Each image processing model may further comprise one or moreregularization components. Regularization components are structured toremediate over-fitting by the model, which leads to a fall in accuracy.Such regularization components may comprise Akaike information criterion(AIC), Bayesian information criterion (BIC), Ridge regression, Lasso,etc. Each image processing model may further comprise one or morelearning rate components. The learning rate is a hyperparameter thatcontrols how much to change the model in response to the estimated erroreach time the model weights are updated. It determines to what extentnewly acquired information overrides old information, i.e., indicateslearning rate decay or momentum. In some embodiments, the learning ratecomponent is a configurable hyperparameter used in the training ofneural networks that has a small positive value, typically in the rangebetween 0.0 and 1.0. In other words, the amount that the weights of themodel are updated during training is referred to as the step size or thelearning rate component. Each image processing model may furthercomprise one or more optimization functions. Optimization functions arestructured to minimize (or maximize) an objective function, i.e., anError function of the model. The optimization function may comprise afirst order optimization type function (e.g., gradient descentfunction), a second order optimization type function (e.g., Hessianfunction), etc.

At block 704, the system may receive a second user input associated withaddition of a second image processing model to the at least one hostedmodel versioning system repository. Here, the system may receive userinput queries comprising a plurality of discrete input language elementshaving the operational discrete element of “add”, such as “add model2 toimagenew”.

In response, the system may determine that the second image processingmodel is a version of the first image processing model, as illustratedby block 706. Here, the system may determine that the second imageprocessing model has been constructed by mutating the first imageprocessing model and consequently infer that the second image processingmodel is a version of the first image processing model. Here, the systemmay determine that the second image processing model is a version of thefirst image processing model based on determining that (i) the firstimage processing model has been copied to or checked out to a localrepository 230 and (ii) the second image processing model has beenconstructed by mutating the first image processing model. The system mayfurther request a user confirmation that the second image processingmodel is a version of the first image processing model.

Next, the system may determine a second plurality of hyper parametersassociated with the second image processing model, at block 708. Thesecond plurality of hyper parameters of the second image processingmodel may be similar to those described above, and may comprise (i) asecond number of convolution neural network layers, i.e., a plurality ofsecond convolutional neural network layers, (ii) a second activationfunction, (iii) a second number of second neurons in the plurality ofsecond convolutional neural network layers, (iv) second loss function,(v) second regularization component, (vi) second learning rate componentof the second image processing model, (vii) type of optimizationfunction, and/or other hyperparameters.

The system may then correlate hyperparameters of the second imageprocessing model with the corresponding hyperparameters of the firstimage processing model. The system may then map the mutations in hyperparameters between the first plurality of hyper parameters of the firstimage processing model and the second plurality of hyper parametersassociated with the second images processing model, as illustrated byblock 710.

In some embodiments, for mapping the mutations in hyper parameters, thesystem may determine that the plurality of first convolutional neuralnetwork layers of the first image processing model is associated with afirst number of convolutional neural network layers. The system mayfurther determine that the plurality of second convolutional neuralnetwork layers of the second image processing model is associated with asecond number of convolutional neural network layers. In response, thesystem may determine at least one mutation based on determining that thesecond number of convolutional neural network layers is different fromthe first number of convolutional neural network layers, e.g., based onidentifying that one or more layers have been inserted or removed. Thesystem may then map the convolutional neural network layer typehyperparameters of the first image processing model and the second imageprocessing model indicating the at least one mutation.

In some embodiments, for mapping the mutations in hyper parameters, thesystem may determine that the first activation function of the firstimage processing model is associated with a first type of non-linearactivation, such as ReLU type activation. The system may furtherdetermine that the second activation function of the second imageprocessing model is associated with a second type of non-linearactivation, such as Softmax type function. The system may determine atleast one mutation based on determining that the first type ofnon-linear activation is different from the second type of non-linearactivation. The system may then map convolutional neural network layertype hyperparameters of the first image processing model and the secondimage processing model indicating the at least one mutation.

In some embodiments, for mapping the mutations in hyper parameters, thesystem may determine that a particular layer of the first imageprocessing model is associated with a first number of neurons. Thesystem may further determine that a corresponding layer of the secondimage processing model is associated with a second number of neurons. Inresponse, the system may determine at least one mutation based ondetermining that the second number of neurons is different from thefirst number of neurons. The system may then map the neuron number typehyperparameters of the first image processing model and the second imageprocessing model indicating the at least one mutation.

In some embodiments, for mapping the mutations in hyper parameters, thesystem may determine that the first loss function of the first imageprocessing model is associated with a first type. The system may furtherdetermine that the second loss function of the second image processingmodel is associated with a second type. The system may determine atleast one mutation based on determining that the first type of lossfunction is different from the second type of loss function. The systemmay then map loss function type hyperparameters of the first imageprocessing model and the second image processing model indicating the atleast one mutation.

In some embodiments, for mapping the mutations in hyper parameters, thesystem may determine that the first regularization component of thefirst image processing model is associated with a first type. The systemmay further determine that the second regularization component of thesecond image processing model is associated with a second type. Thesystem may determine at least one mutation based on determining that thefirst type of regularization component is different from the second typeof regularization component. The system may then map regularizationcomponent type hyperparameters of the first image processing model andthe second image processing model indicating the at least one mutation.

In some embodiments, for mapping the mutations in hyper parameters, thesystem may determine that the first learning rate component of the firstimage processing model is associated with a first type. The system mayfurther determine that the second learning rate component of the secondimage processing model is associated with a second type. The system maydetermine at least one mutation based on determining that the first typeof learning rate component is different from the second type of learningrate component. The system may then map learning rate component typehyperparameters of the first image processing model and the second imageprocessing model indicating the at least one mutation.

In some embodiments, for mapping the mutations in hyper parameters, thesystem may determine that the first optimization function component ofthe first image processing model is associated with a first type. Thesystem may further determine that the second optimization functioncomponent of the second image processing model is associated with asecond type. The system may determine at least one mutation based ondetermining that the first type of optimization function component isdifferent from the second type of optimization function component. Thesystem may then map optimization function component type hyperparametersof the first image processing model and the second image processingmodel indicating the at least one mutation.

In this manner, the system may analyze all hyperparameters to identifyand map mutations, e.g., in a sequential order.

The system may subsequently present, on a display device of the userdevice, a graphical representation comprising the mapped mutation inhyper parameters between the first plurality of hyper parameters of thefirst image processing model and the second plurality of hyperparameters associated with the second images processing model, asillustrated by block 712. Here, the system may present a representationsimilar to that of FIG. 3 and/or a representation similar to that ofFIG. 4, and the mapped mutations may be overlaid over the depictions byinserting highlighting elements in a graphical representation of thesecond image processing model indicating the mutations between the firstplurality of hyper parameters of the first image processing model andthe second plurality of hyper parameters associated with the secondimage processing model.

Moreover, e.g., in response to receiving the second user inputassociated with addition of a second image processing model to the atleast one hosted model versioning system repository, the system mayfurther construct a hierarchical linked architecture for the secondimage processing model. As discussed previously, the hierarchical linkedarchitecture for the second image processing model comprises asequential linked arrangement of a plurality of second convolutionneural network layers associated with the second image processing model,as illustrated by FIG. 4. Moreover, the system may further construct theweigh parameter objects associated with the plurality of secondconvolution neural network layers of the second image processing model.Typically, the weigh parameter objects are constructed such that thesecond image processing model can be reconstructed from the weighparameter objects.

The system may then compress the second image processing model forstorage using the parameter archival storage (also referred to asparametrized storage) described previously. The system may process afirst plurality of weights (i.e., floating point weights as describedwith respect to FIG. 4) associated with the first plurality of hyperparameters of the first image processing model and a correspondingsecond plurality of weights (i.e., floating point weights as describedwith respect to FIG. 4) associated with the second plurality of hyperparameters of the second image processing model. Here, the system maythen determine altered weights in the second plurality of weights thatdeviate from the corresponding first plurality of weights. The systemmay map the altered weights in the second plurality of weights with thecorresponding first plurality of weights and the corresponding pluralityof first convolutional neural network layers. Hence, in this manner thesystem may construct the weigh parameter objects for the second imageprocessing model comprising the altered weights. Indeed, here theconstructed weigh parameter objects for the second image processingmodel are the only altered weights (the rest are discarded).

Specifically, the second image processing model can be stored in acompressed parameterized manner by merely storing the (i) only the weighparameter objects (floating point weights) of the second imageprocessing model that differ from that of the first image processingmodel, and (ii) the mapped mutations in hyper parameters, instead of theentire hierarchical linked architecture framework of the second imageprocessing model. In some embodiments, based on mapping the mutations inhyper parameters between the first plurality of hyper parameters of thefirst image processing model and the second plurality of hyperparameters associated with the second image processing model, the systemmay discard the hierarchical linked architecture of the second imageprocessing model. The system may store the second image processing modelat the at least one hosted model versioning system repository by storingonly (i) the weigh parameter objects, and (ii) mapped mutations in hyperparameters.

In some embodiments, the system is further configured to dynamicallyreconstruct the compressed second image processing model, e.g., based onreceiving another user input for selecting the model for analysis. Here,the system may extract (i) the weigh parameter objects, and (ii) themapped mutations in hyper parameters of the second image processingmodel. The system may then map (i) the weigh parameter objects, and (ii)the mapped mutations in hyper parameters with an original hierarchicallinked architecture of the first image processing model to construct thesecond image processing model, and subsequently present the dynamicallyreconstructed second image processing model on the display device of theuser device.

FIG. 8, illustrates a high level process flow 800 for a parameterarchival storage system for image processing models, in accordance withone embodiment of the present invention. Here, the system is configuredfor read-optimized compression storage of machine-learningneural-network based image processing models with reduced storage byseparately storing weight filter bits. As discussed previously, neuralnetwork based machine-learning models typically have a complexarchitecture involving numerous interconnected layers, components andfunctions. Storing and managing (e.g., checking-in and checking-out,versioning, etc.) such complex neural network based machine-learningmodels and their artifacts is cumbersome, and requires large amounts ofmemory. Embodiments of the invention provide solutions to theseproblems. Specifically, embodiments provide a parameter archival storagesystem configured for read-optimized compression storage ofmachine-learning neural-network based image processing models withreduced storage. Here, the parameter archival storage system isconfigured for determining and using deltas (.e.g., mutated/alteredweights filters) in the structure of neural network basedmachine-learning models. This novel storing system is configured forreducing/compressing the storage requirements for a neural network basedmachine-learning model from storing complex architecture involvingnumerous interconnected layers, components and functions, to storingmere floating point bits, providing data compression in the range of100,000:1, 10,000:1, 1000:1, etc., without losing any accuracy of themodel.

First at block 802, the system may receive, from a user device, a firstuser input to check-out a first image processing model from the at leastone hosted model versioning system repository. As discussed, the firstimage processing model comprises a plurality of first convolutionalneural network layers. The system may then extract the first imageprocessing model of the one or more image processing models. Here,“check-out” may refer to extracting the model (i.e., checking-out) fromthe MVS repository(s) 260 to the local repository 230 or the user device104. Here, the user may seek to check-out an existing first imageprocessing model in the hosted MVS repository(s) 260, e.g., for thepurposes of mutating the model. Here, the user may input user queriescontaining a plurality of discrete elements (e.g., similar to thequeries (510, 520) illustrated in FIG. 5) each containing naturallanguage strings. These user queries comprise operational discreteelements (512 a, 522 a) for extracting the model (i.e., checking-out)from the MVS repository(s) 260 to the local repository 230/user device104, comprising commands such as: “search” for searching/selectingmodels in the MVS repository 260 to locate/identify the desired firstimage processing model, “pull” for downloading model files, etc. Basedon receiving these queries from the user device 104, the MVS module 212may transmit instructions to the image processing model control server250 to retrieve the files via the image processing DL model searchcomponent 254, and subsequently deliver the model files (e.g., afterdynamically reconstructing the model) to the user device 104. In thismanner, the system may extract the first image processing model of theone or more image processing models.

At block 804, the system may receive a second user input associated witha request to store a second image processing model at least one hostedmodel versioning system repository. Here, the system may then receiveuser input queries comprising a plurality of discrete input languageelements having the operational discrete element of “add”, such as “addmodel2 to imagenew”.

In response, the system may determine a hierarchical linked architectureassociated with the second image processing model, as illustrated byblock 806. Typically, the hierarchical linked architecture comprises asequential linked arrangement of a plurality of second convolutionneural network layers associated with the second image processing model,as described with respect to FIGS. 3-4, and 7.

Next, at block 808, the system may construct weigh parameter objectsassociated with the plurality of second convolution neural networklayers of the second image processing model. Typically, the weighparameter objects are constructed such that the second image processingmodel can be reconstructed from the weigh parameter objects. Here, thesystem may first extract a first plurality of weights associated withthe plurality of first convolutional neural network layers of the firstimage processing model. The system may then extract a second pluralityof weights associated with the plurality of second convolution neuralnetwork layers associated with the second image processing model. Thesystem may then determine altered weights in the second plurality ofweights that deviate from the corresponding first plurality of weights.Subsequently, the system may map the altered weights in the secondplurality of weights with the corresponding first plurality of weightsand the corresponding plurality of first convolutional neural networklayers. In this manner, the system may construct the weigh parameterobjects for the second image processing model comprising the alteredweights.

The system may then discard the hierarchical linked architecture of thesecond image processing model at block 810. In this regard, thehierarchical linked architecture of the second image processing modelthat may have been stored until then at a temporary or transient memorylocation is purged.

At block 812, the system may store the second image processing model atthe at least one hosted model versioning system repository by storingonly the weigh parameter objects and present, on a display device of theuser device, an indication that the second image processing model hasbeen stored. Here, the system may first map a first hyper parameter ofthe plurality of first convolutional neural network layers of the firstimage processing model with a second hyper parameter of the plurality ofsecond convolution neural network layers associated with the secondimage processing model, based on determining that the second hyperparameter is a mutation of the original first hyper parameter. Thesystem may then storing the second image processing model at the atleast one hosted model versioning system repository by storing only (i)the weigh parameter objects, and (ii) the second hyper parameter.

In some embodiments, the system is further configured to construct apointer link between the weigh parameter objects and a storedhierarchical linked architecture associated with the first imageprocessing model, e.g., prior to discarding the hierarchical linkedarchitecture. This pointer line may be utilized for reconstructing themodel later on.

In some embodiments, the system is further configured to dynamicallyreconstruct the compressed second image processing model, e.g., based onreceiving another user input for selecting the model for analysis. Here,the system may extract the weigh parameter objects. The system may thenmap the weigh parameter objects with a stored hierarchical linkedarchitecture of the first image processing model to construct the secondimage processing model. The system may then subsequently present thedynamically reconstructed second image processing model on the displaydevice of the user device.

In some embodiments, the system is configured to dynamically reconstructthe compressed second image processing model my extracting (i) the weighparameter objects, and (ii) the mapped mutations in hyper parameters ofthe second image processing model. The system may then map (i) the weighparameter objects, and (ii) the mapped mutations in hyper parameterswith an original hierarchical linked architecture of the first imageprocessing model to construct the second image processing model, andsubsequently present the dynamically reconstructed second imageprocessing model on the display device of the user device.

As will be appreciated by one of ordinary skill in the art, the presentinvention may be embodied as an apparatus (including, for example, asystem, a machine, a device, a computer program product, and/or thelike), as a method (including, for example, a business process, acomputer-implemented process, and/or the like), or as any combination ofthe foregoing. Accordingly, embodiments of the present invention maytake the form of an entirely software embodiment (including firmware,resident software, micro-code, and the like), an entirely hardwareembodiment, or an embodiment combining software and hardware aspectsthat may generally be referred to herein as a “system.” Furthermore,embodiments of the present invention may take the form of a computerprogram product that includes a computer-readable storage medium havingcomputer-executable program code portions stored therein. As usedherein, a processor may be “configured to” perform a certain function ina variety of ways, including, for example, by having one or morespecial-purpose circuits perform the functions by executing one or morecomputer-executable program code portions embodied in acomputer-readable medium, and/or having one or more application-specificcircuits perform the function.

It will be understood that any suitable computer-readable medium may beutilized. The computer-readable medium may include, but is not limitedto, a non-transitory computer-readable medium, such as a tangibleelectronic, magnetic, optical, infrared, electromagnetic, and/orsemiconductor system, apparatus, and/or device. For example, in someembodiments, the non-transitory computer-readable medium includes atangible medium such as a portable computer diskette, a hard disk, arandom access memory (RAM), a read-only memory (ROM), an erasableprogrammable read-only memory (EPROM or Flash memory), a compact discread-only memory (CD-ROM), and/or some other tangible optical and/ormagnetic storage device. In other embodiments of the present invention,however, the computer-readable medium may be transitory, such as apropagation signal including computer-executable program code portionsembodied therein.

It will also be understood that one or more computer-executable programcode portions for carrying out the specialized operations of the presentinvention may be required on the specialized computer includeobject-oriented, scripted, and/or unscripted programming languages, suchas, for example, Java, Perl, Smalltalk, C++, SAS, SQL, Python, ObjectiveC, and/or the like. In some embodiments, the one or morecomputer-executable program code portions for carrying out operations ofembodiments of the present invention are written in conventionalprocedural programming languages, such as the “C” programming languagesand/or similar programming languages. The computer program code mayalternatively or additionally be written in one or more multi-paradigmprogramming languages, such as, for example, F#.

It will further be understood that some embodiments of the presentinvention are described herein with reference to flowchart illustrationsand/or block diagrams of systems, methods, and/or computer programproducts. It will be understood that each block included in theflowchart illustrations and/or block diagrams, and combinations ofblocks included in the flowchart illustrations and/or block diagrams,may be implemented by one or more computer-executable program codeportions.

It will also be understood that the one or more computer-executableprogram code portions may be stored in a transitory or non-transitorycomputer-readable medium (e.g., a memory, and the like) that can directa computer and/or other programmable data processing apparatus tofunction in a particular manner, such that the computer-executableprogram code portions stored in the computer-readable medium produce anarticle of manufacture, including instruction mechanisms which implementthe steps and/or functions specified in the flowchart(s) and/or blockdiagram block(s).

The one or more computer-executable program code portions may also beloaded onto a computer and/or other programmable data processingapparatus to cause a series of operational steps to be performed on thecomputer and/or other programmable apparatus. In some embodiments, thisproduces a computer-implemented process such that the one or morecomputer-executable program code portions which execute on the computerand/or other programmable apparatus provide operational steps toimplement the steps specified in the flowchart(s) and/or the functionsspecified in the block diagram block(s). Alternatively,computer-implemented steps may be combined with operator and/orhuman-implemented steps in order to carry out an embodiment of thepresent invention.

While certain exemplary embodiments have been described and shown in theaccompanying drawings, it is to be understood that such embodiments aremerely illustrative of, and not restrictive on, the broad invention, andthat this invention not be limited to the specific constructions andarrangements shown and described, since various other changes,combinations, omissions, modifications and substitutions, in addition tothose set forth in the above paragraphs, are possible. Those skilled inthe art will appreciate that various adaptations and modifications ofthe just described embodiments can be configured without departing fromthe scope and spirit of the invention. Therefore, it is to be understoodthat, within the scope of the appended claims, the invention may bepracticed other than as specifically described herein.

To supplement the present disclosure, this application furtherincorporates entirely by reference the following commonly assignedpatent applications:

Docket Number U.S. patent application Ser. No. Title Filed On8897US1.014033.3450 To be assigned ELECTRONIC QUERY Concurrently ENGINEFOR AN IMAGE herewith PROCESSING MODEL DATABASE 9192US1.014033.3489 Tobe assigned ELECTRONIC SYSTEM FOR Concurrently MANAGEMENT OF IMAGEherewith PROCESSING MODELS

1. A parameter archival storage system for image processing models,wherein the system is configured for read-optimized compression storageof machine-learning neural-network based image processing models withreduced storage by separately storing weight filter bits, the systemcomprising: at least one hosted model versioning system repositorycomprising one or more image processing models stored thereon, whereineach of the one or more image processing models are configured forhierarchical processing of temporal image data via at least oneconvolutional neural network; at least one memory device withcomputer-readable program code stored thereon; at least onecommunication device; at least one processing device operatively coupledto the at least one memory device and the at least one communicationdevice, wherein executing the computer-readable code is configured tocause the at least one processing device to: receive, from a userdevice, a first user input to check-out a first image processing modelfrom the at least one hosted model versioning system repository, whereinthe first image processing model comprises a plurality of firstconvolutional neural network layers; extract the first image processingmodel of the one or more image processing models; receive, from the userdevice, a second user input associated with a request to store a secondimage processing model at least one hosted model versioning systemrepository; determine a hierarchical linked architecture associated withthe second image processing model, wherein the hierarchical linkedarchitecture comprises a sequential linked arrangement of a plurality ofsecond convolution neural network layers associated with the secondimage processing model; construct weigh parameter objects associatedwith the plurality of second convolution neural network layers of thesecond image processing model, wherein the weigh parameter objects areconstructed such that the second image processing model can bereconstructed from the weigh parameter objects; discard the hierarchicallinked architecture of the second image processing model; and store thesecond image processing model at the at least one hosted modelversioning system repository by storing only the weigh parameterobjects; and present, on a display device of the user device, anindication that the second image processing model has been stored. 2.The system of claim 1, wherein constructing weigh parameter objectsassociated with the second image processing model further comprises:extracting a first plurality of weights associated with the plurality offirst convolutional neural network layers of the first image processingmodel; extracting a second plurality of weights associated with theplurality of second convolution neural network layers associated withthe second image processing model; determining altered weights in thesecond plurality of weights that deviate from the corresponding firstplurality of weights; mapping the altered weights in the secondplurality of weights with the corresponding first plurality of weightsand the corresponding plurality of first convolutional neural networklayers; and constructing the weigh parameter objects for the secondimage processing model comprising the altered weights.
 3. The system ofclaim 1, wherein executing the computer-readable code is configured tofurther cause the at least one processing device to: receive, from theuser device, a third user input associated with selection of theconstructed second image processing model for analysis; dynamicallyreconstruct the second image processing model by: extracting the weighparameter objects; and mapping the weigh parameter objects with a storedhierarchical linked architecture of the first image processing model toconstruct the second image processing model; and present the dynamicallyreconstructed second image processing model on the display device of theuser device.
 4. The system of claim 1, wherein storing the second imageprocessing model at the at least one hosted model versioning systemrepository further comprises: mapping a first hyper parameter of theplurality of first convolutional neural network layers of the firstimage processing model with a second hyper parameter of the plurality ofsecond convolution neural network layers associated with the secondimage processing model, based on determining that the second hyperparameter is a mutation of the original first hyper parameter; andstoring the second image processing model at the at least one hostedmodel versioning system repository by storing only (i) the weighparameter objects, and (ii) the second hyper parameter.
 5. The system ofclaim 4, wherein executing the computer-readable code is configured tofurther cause the at least one processing device to: receive, from theuser device, a third user input associated with selection of theconstructed second image processing model for analysis; dynamicallyreconstruct the second image processing model by: extracting the (i) theweigh parameter objects, and (ii) the mapped mutation in hyperparameters; and mapping the (i) the weigh parameter objects, and (ii)the mapped mutation in hyper parameters with a stored hierarchicallinked architecture of the first image processing model to construct thesecond image processing model; and present the dynamically reconstructedsecond image processing model on the display device of the user device.6. The system of claim 1, wherein storing the second image processingmodel at the at least one hosted model versioning system repositoryfurther comprises: constructing a pointer link between the weighparameter objects and a stored hierarchical linked architectureassociated with the first image processing model.
 7. The system of claim1, wherein executing the computer-readable code is configured to furthercause the at least one processing device to perform training of thesecond image processing model.
 8. A computer program product for anparameter archival storage system for image processing models, whereinthe computer program product is configured for read-optimizedcompression storage of machine-learning neural-network based imageprocessing models with reduced storage by separately storing weightfilter bits, the computer program product comprising a non-transitorycomputer-readable storage medium having computer-executable instructionsthat when executed by a processing device are configured to cause theprocessing device to: receive, from a user device, a first user input tocheck-out a first image processing model from the at least one hostedmodel versioning system repository, wherein the first image processingmodel comprises a plurality of first convolutional neural networklayers; extract the first image processing model of the one or moreimage processing models; receive, from the user device, a second userinput associated with a request to store a second image processing modelat least one hosted model versioning system repository; determine ahierarchical linked architecture associated with the second imageprocessing model, wherein the hierarchical linked architecture comprisesa sequential linked arrangement of a plurality of second convolutionneural network layers associated with the second image processing model;construct weigh parameter objects associated with the plurality ofsecond convolution neural network layers of the second image processingmodel, wherein the weigh parameter objects are constructed such that thesecond image processing model can be reconstructed from the weighparameter objects; discard the hierarchical linked architecture of thesecond image processing model; and store the second image processingmodel at the at least one hosted model versioning system repository bystoring only the weigh parameter objects; and present, on a displaydevice of the user device, an indication that the second imageprocessing model has been stored.
 9. The computer program product ofclaim 8, wherein constructing weigh parameter objects associated withthe second image processing model further comprises: extracting a firstplurality of weights associated with the plurality of firstconvolutional neural network layers of the first image processing model;extracting a second plurality of weights associated with the pluralityof second convolution neural network layers associated with the secondimage processing model; determining altered weights in the secondplurality of weights that deviate from the corresponding first pluralityof weights; mapping the altered weights in the second plurality ofweights with the corresponding first plurality of weights and thecorresponding plurality of first convolutional neural network layers;and constructing the weigh parameter objects for the second imageprocessing model comprising the altered weights.
 10. The computerprogram product of claim 8, wherein the non-transitory computer-readablestorage medium further comprises computer-executable instructions thatwhen executed by the processing device are configured to cause theprocessing device to: receive, from the user device, a third user inputassociated with selection of the constructed second image processingmodel for analysis; dynamically reconstruct the second image processingmodel by: extracting the weigh parameter objects; and mapping the weighparameter objects with a stored hierarchical linked architecture of thefirst image processing model to construct the second image processingmodel; and present the dynamically reconstructed second image processingmodel on the display device of the user device.
 11. The computer programproduct of claim 8, wherein storing the second image processing model atthe at least one hosted model versioning system repository furthercomprises: mapping a first hyper parameter of the plurality of firstconvolutional neural network layers of the first image processing modelwith a second hyper parameter of the plurality of second convolutionneural network layers associated with the second image processing model,based on determining that the second hyper parameter is a mutation ofthe original first hyper parameter; and storing the second imageprocessing model at the at least one hosted model versioning systemrepository by storing only (i) the weigh parameter objects, and (ii) thesecond hyper parameter.
 12. The computer program product of claim 11,wherein the non-transitory computer-readable storage medium furthercomprises computer-executable instructions that when executed by theprocessing device are configured to cause the processing device to:receive, from the user device, a third user input associated withselection of the constructed second image processing model for analysis;dynamically reconstruct the second image processing model by: extractingthe (i) the weigh parameter objects, and (ii) the mapped mutation inhyper parameters; and mapping the (i) the weigh parameter objects, and(ii) the mapped mutation in hyper parameters with a stored hierarchicallinked architecture of the first image processing model to construct thesecond image processing model; and present the dynamically reconstructedsecond image processing model on the display device of the user device.13. The computer program product of claim 8, wherein storing the secondimage processing model at the at least one hosted model versioningsystem repository further comprises: constructing a pointer link betweenthe weigh parameter objects and a stored hierarchical linkedarchitecture associated with the first image processing model.
 14. Thecomputer program product of claim 8, wherein the non-transitorycomputer-readable storage medium further comprises computer-executableinstructions that when executed by the processing device are configuredto cause the processing device to perform training of the second imageprocessing model.
 15. A computerized method for an parameter archivalstorage system for image processing models, wherein the computerizedmethod is configured for read-optimized compression storage ofmachine-learning neural-network based image processing models withreduced storage by separately storing weight filter bits, thecomputerized method comprising: receiving, from a user device, a firstuser input to check-out a first image processing model from at least onehosted model versioning system repository, wherein the first imageprocessing model comprises a plurality of first convolutional neuralnetwork layers; extracting the first image processing model of the oneor more image processing models; receiving, from the user device, asecond user input associated with a request to store a second imageprocessing model at least one hosted model versioning system repository;determining a hierarchical linked architecture associated with thesecond image processing model, wherein the hierarchical linkedarchitecture comprises a sequential linked arrangement of a plurality ofsecond convolution neural network layers associated with the secondimage processing model; constructing weigh parameter objects associatedwith the plurality of second convolution neural network layers of thesecond image processing model, wherein the weigh parameter objects areconstructed such that the second image processing model can bereconstructed from the weigh parameter objects; discarding thehierarchical linked architecture of the second image processing model;and storing the second image processing model at the at least one hostedmodel versioning system repository by storing only the weigh parameterobjects; and presenting, on a display device of the user device, anindication that the second image processing model has been stored. 16.The computerized method of claim 15, wherein constructing weighparameter objects associated with the second image processing modelfurther comprises: extracting a first plurality of weights associatedwith the plurality of first convolutional neural network layers of thefirst image processing model; extracting a second plurality of weightsassociated with the plurality of second convolution neural networklayers associated with the second image processing model; determiningaltered weights in the second plurality of weights that deviate from thecorresponding first plurality of weights; mapping the altered weights inthe second plurality of weights with the corresponding first pluralityof weights and the corresponding plurality of first convolutional neuralnetwork layers; and constructing the weigh parameter objects for thesecond image processing model comprising the altered weights.
 17. Thecomputerized method of claim 15, wherein the method further comprises:receiving, from the user device, a third user input associated withselection of the constructed second image processing model for analysis;dynamically reconstructing the second image processing model by:extracting the weigh parameter objects; and mapping the weigh parameterobjects with a stored hierarchical linked architecture of the firstimage processing model to construct the second image processing model;and presenting the dynamically reconstructed second image processingmodel on the display device of the user device.
 18. The computerizedmethod of claim 15, wherein storing the second image processing model atthe at least one hosted model versioning system repository furthercomprises: mapping a first hyper parameter of the plurality of firstconvolutional neural network layers of the first image processing modelwith a second hyper parameter of the plurality of second convolutionneural network layers associated with the second image processing model,based on determining that the second hyper parameter is a mutation ofthe original first hyper parameter; and storing the second imageprocessing model at the at least one hosted model versioning systemrepository by storing only (i) the weigh parameter objects, and (ii) thesecond hyper parameter.
 19. The computerized method of claim 18, whereinthe method further comprises: receiving, from the user device, a thirduser input associated with selection of the constructed second imageprocessing model for analysis; dynamically reconstructing the secondimage processing model by: extracting the (i) the weigh parameterobjects, and (ii) the mapped mutation in hyper parameters; and mappingthe (i) the weigh parameter objects, and (ii) the mapped mutation inhyper parameters with a stored hierarchical linked architecture of thefirst image processing model to construct the second image processingmodel; and presenting the dynamically reconstructed second imageprocessing model on the display device of the user device.
 20. Thecomputerized method of claim 15, wherein storing the second imageprocessing model at the at least one hosted model versioning systemrepository further comprises: constructing a pointer link between theweigh parameter objects and a stored hierarchical linked architectureassociated with the first image processing model.